Boosting ML Model Interoperability and Efficiency with the ONNX framework

AI Engineering /

The rapid growth of artificial intelligence and machine learning has led to the development of numerous deep learning frameworks. Each framework has its strengths and weaknesses, making it challenging to deploy models across different platforms. However, the Open Neural Network Exchange (ONNX) framework has emerged as a powerful solution to this problem. This article introduces the ONNX framework, explains its basics, and highlights the benefits of using it.

Understanding the basics of ONNX

What is ONNX? The Open Neural Network Exchange (ONNX) is an open-source framework that enables the seamless interchange of models between different deep learning frameworks. It provides a standardized format for representing trained models, allowing them to be transferred and executed on various platforms. ONNX allows you to train your models using one framework and then deploy them using a different framework, eliminating the need for time-consuming and error-prone model conversions.

ONXX framework interoperability

Why use ONNX? There are several significant benefits of using the ONNX framework. First and foremost, it enhances model interoperability. By providing a standardized model format, ONNX enables seamless integration between different deep learning frameworks, such as PyTorch, TensorFlow, Keras, and Caffe. This interoperability allows researchers and developers to leverage the strengths of multiple frameworks and choose the one that best suits their specific needs.

Advantages of using the ONNX framework

ONNX support and capabilities across platforms: One of the major advantages of the ONNX framework is its wide support and capabilities across platforms. ONNX models can be deployed on a variety of devices and platforms, including CPUs, GPUs, and edge devices. This flexibility allows you to leverage the power of deep learning across a range of hardware, from high-performance servers to resource-constrained edge devices.

Simplified deployment: ONNX simplifies the deployment process by eliminating the need for model conversion. With ONNX, you can train your models in your preferred deep learning framework and then export them directly to ONNX format. This saves time and reduces the risk of introducing errors during the conversion process.

Efficient execution: The framework provides optimized runtimes that ensure fast and efficient inference across different platforms. This means that your models can deliver high-performance results, even on devices with limited computational resources. By using ONNX, you can maximize the efficiency of your deep learning models without compromising accuracy or speed.

Enhancing model interoperability with ONNX

ONNX goes beyond just enabling model interoperability. It also provides a rich ecosystem of tools and libraries that further enhance the interoperability between different deep learning frameworks. For example, ONNX Runtime is a high-performance inference engine that allows you to seamlessly execute ONNX models on a wide range of platforms. It provides support for a variety of hardware accelerators, such as GPUs and FPGAs, enabling you to unlock the full potential of your models.

ONNX Runtime

Moreover, ONNX also supports model optimization and quantization techniques. These techniques can help reduce the size of your models, making them more efficient to deploy and run on resource-constrained devices. By leveraging the optimization and quantization capabilities of ONNX, you can ensure that your models are not only interoperable but also highly efficient.

Improving efficiency with the ONNX framework

Efficiency is a critical factor in deep learning, especially when dealing with large-scale models and vast amounts of data. The ONNX framework offers several features that can help improve the efficiency of models and streamline the development process.

One such feature is the ONNX Model Zoo, which provides a collection of pre-trained models that anyone can use as a starting point for projects. These models cover a wide range of domains and tasks, including image classification, object detection, and natural language processing. By leveraging pre-trained models from the ONNX Model Zoo, it saves time and computational resources, allowing to focus on fine-tuning the models for specific needs.

Another efficiency-enhancing feature of ONNX is its support for model compression techniques. Model compression aims to reduce the size of deep learning models without significant loss in performance. ONNX provides tools and libraries that enable you to apply compression techniques, such as pruning, quantization, and knowledge distillation, to your models. By compressing the models with ONNX, you can achieve smaller model sizes, faster inference times, and reduced memory requirements.

Let us see successful implementations of ONNX

To understand the real-world impact of the ONNX framework, let’s look at some use cases where it has been successfully implemented.
Facebook AI Research used ONNX to improve the efficiency of their deep learning models for image recognition. By converting their models to the ONNX format, they were able to deploy them on a range of platforms, including mobile devices and web browsers. This improved the accessibility of their models and allowed them to reach a wider audience.

Microsoft utilized ONNX to optimize their machine learning models for speech recognition. By leveraging the ONNX Runtime, they achieved faster and more efficient inference on various platforms, enabling real-time speech-to-text transcription in their applications.
These use cases demonstrate the versatility and effectiveness of the ONNX framework in real-world scenarios, highlighting its ability to enhance model interoperability and efficiency.

Challenges and limitations of the ONNX framework

While the ONNX framework offers numerous benefits, it also has its challenges and limitations. One of the main challenges is the discrepancy in supported operators and layers across different deep learning frameworks. Although ONNX aims to provide a comprehensive set of operators, there may still be cases where certain operators are not fully supported or behave differently across frameworks. This can lead to compatibility issues when transferring models between frameworks.

Another limitation of the ONNX framework is the lack of support for dynamic neural networks. ONNX primarily focuses on static computational graphs, which means that models with dynamic structures, such as Recurrent Neural Networks (RNNs) or models with varying input sizes, may not be fully supported.

It is important to carefully consider these challenges and limitations when deciding to adopt the ONNX framework for deep learning projects. However, it is worth noting that the ONNX community is actively working towards addressing these issues and improving the framework’s capabilities.

Future trends and developments in ONNX

The ONNX framework is continuously evolving, with ongoing developments and future trends that promise to further enhance its capabilities. One such development is the integration of ONNX with other emerging technologies, such as federated learning and edge computing. This integration will enable efficient and privacy-preserving model exchange and execution in distributed environments.

Furthermore, the ONNX community is actively working on expanding the set of supported operators and layers, as well as improving the compatibility between different deep learning frameworks. These efforts will further enhance the interoperability and ease of using ONNX framework.

To summarize, The ONNX framework provides a powerful solution to the challenges of model interoperability and efficiency in deep learning. By offering a standardized format for representing models and a rich ecosystem of tools and libraries, ONNX enables seamless integration between different deep learning frameworks and platforms. Its support for model optimization and quantization techniques further enhances the efficiency of deep learning models.

While the ONNX framework has its challenges and limitations, its continuous development and future trends promise to address these issues and expand its capabilities. With the increasing adoption of ONNX in both research and industry, this framework is playing a crucial role in advancing the field of deep learning.

For those seeking to enhance the interoperability and efficiency of the deep learning models, exploring the ONNX framework is highly advisable. With its wide support, powerful capabilities, and vibrant community, ONNX is poised to revolutionize the development and deployment of deep learning models for organizations.

At Softnautics, a MosChip company, our team of AIML experts are dedicated to developing optimized Machine Learning solutions specifically tailored for a diverse array of edge platforms. Our expertise covers FPGA, ASIC, CPUs, GPUs, TPUs, and neural network compilers, ensuring the implementation of efficient and high-performance machine learning solutions based on cognitive computing, computer vision, deep learning, Natural Language Processing (NLP), vision analytics, etc.

Read our success stories related to Artificial Intelligence and Machine Learning services to know more about our expertise under AIML.

Contact us at business@softnautics.com for any queries related to your solution design or for consultancy.

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Revolutionizing Consumer Electronics with the power of AI Integration

AI Engineering /

In recent years, the rapid advancement of technology has revolutionized various industries, and the consumer electronics sector is no exception. One of the most prominent and influential technologies is Artificial Intelligence (AI) and Machine Learning (ML) development. AI-powered technology, driven by machine learning advancements, has a profound impact on consumer electronics, transforming our interaction with consumer devices/ products. To enable these devices to analyse data, learn from it, and make decisions or take actions based on that analysis, intelligent algorithms and machine learning techniques are used.

Consumer electronics encompass a wide range of electronic devices that are intended for personal usage and entertainment purposes. This includes smartphones, tablets, laptops, televisions, smartwatches, and more. The sector has experienced significant growth over the years, with consumers becoming increasingly reliant on these devices for communication, information, and entertainment.

Evolution of AI in Consumer Electronics

AI integration into consumer electronics began with voice recognition. Devices such as smartphones and personal assistants implement AI algorithms to understand and respond to user commands. AI has transformed consumer electronics devices into smart, intuitive, and personalized companions that enhance our daily lives. This transformation is influenced by the advancement of microprocessors or AI-enabled chips. Microprocessors, often referred to as the “brain” of electronic devices, play a vital role in providing AI capabilities in consumer electronics. Over the years, AI-enabled chips have become more powerful and energy-efficient, allowing for the integration of AI algorithms directly into consumer electronic devices. This integration has led to significant advancements in voice recognition, natural language processing, and machine learning capabilities. As AI technology advanced, so did its impact on consumer electronics. One notable development was the emergence of voice assistants. AI-powered assistants became common, residing on smart speakers, smartphones, and other devices, providing users with a wide range of flexibility and convenience. It could answer questions, set reminders, play music, control smart home devices, and perform various other tasks, all through voice commands. These significant advancements in artificial intelligence and machine learning solutions have paved the way for more sophisticated and innovative applications in the consumer electronics sector.

Impact of AI on the Consumer Electronics Market

The integration of AI-powered technology has had a significant impact on the consumer electronics market, shaping consumer expectations, evolving business models, and creating new market opportunities. As consumers become increasingly familiar with smart devices in their daily lives, their expectations and demands for smart and intuitive electronics are growing. They expect seamless integration, personalized experiences, and enhanced functionality.

The integration of AI into consumer electronics has brought about significant disruptions in traditional consumer industry and simultaneously created new market opportunities. One notable example is the rise of smart home automation. This has revolutionized the way people manage their homes and created new markets for next-gen devices/solutions. Smart home automation refers to the integration of connected devices and systems that allow homeowners to control and monitor various aspects of their homes remotely. Using AI algorithms and connectivity technologies, such as Internet of Things (IoT) devices, smart homes enable seamless integration and automation of household tasks and functions. For example, the increased demand for smart home automation has created a market for home security systems/devices. AI-powered security systems can detect and respond to potential threats, providing homeowners with enhanced safety and peace of mind. These systems can include features such as motion detection via sensors, video surveillance, and automated alerts to prevent unauthorized access or detect suspicious activities.

Another market opportunity that has grown from smart home automation is in the field of energy management solutions. AI algorithms can analyze energy usage patterns within a home and provide recommendations for optimizing energy consumption. Smart thermostats, for instance, can learn the preferences and behavior of occupants and adjust temperature settings accordingly, leading to energy savings and increased efficiency. Additionally, AI-powered systems can monitor energy consumption and suggest ways to reduce wastage, such as turning off lights or appliances when they are not in use.

Applications of consumer electronics

Applications of AI in Consumer Electronics

AI has found a wide range of applications, enhancing user experiences and product functionality in connected consumer electronics.

Voice assistants and smart speakers: AI enabled voice assistants and connected applications have become an integral part of many homes, with smart speakers like Amazon Echo and Google Home being widely adopted. These voice assistants rely heavily on AIML algorithms to understand natural language commands and perform a wide range of tasks. Through Natural Language Processing (NLP) and machine learning, voice assistants can accurately interpret user queries, provide relevant responses, and execute various actions. They can set reminders, play music, answer questions, control smart home devices, and even engage in conversational interactions.

AI-driven audio and video processing: AI is improving audio and video processing in consumer electronics through intelligent algorithms. These algorithms are employed to improve sound quality, reduce background noise, enhance voice clarity, and provide immersive audio experiences. Noise cancellation techniques, powered by AIML, minimize unwanted sounds, and provide clear audio. AIML models can be trained to compare high-resolution and low-resolution video frames. By doing so, these models learn to understand the relationship between the two types of frames. This understanding allows them to generate high-resolution frames from low-resolution inputs, improving overall video quality. These models are called super-resolution algorithms because they enhance video resolution and details. Through the use of advanced AIML techniques, these algorithms play a significant role in upscaling video quality, providing sharper and more visually appealing videos.

Smart IoT wearables: Smart wearables are taking health monitoring to new heights. Advanced sensors combined with AIML algorithms will enable devices to track vital signs, detect anomalies, and provide proactive health insights. IoT Wearables are playing a crucial role in preventive healthcare, empowering users to monitor their well-being and make informed decisions about their health.

The future of AI-powered consumer electronics

AI-driven consumer electronics technology looks promising. AIML algorithms are increasingly being deployed on devices, allowing for faster processing and a reduced reliance on cloud services. This enables real-time decision-making with improved data privacy.

The consumer electronics sector will continue to evolve with AIML technology. We can expect further advancements in all the industries with next-gen smart devices providing improved productivity and personalized experiences. Additionally, AIML integration in IoT wearables and health-related devices is expected to grow, enabling real time monitoring and analysis of user data. As the field continues to evolve exponentially, it is crucial for manufacturers and users to collaborate and navigate the future of AI in consumer electronics responsibly.

At Softnautics, a MosChip company, our AI engineering and machine learning services empower businesses to develop intelligent solutions involving expertise over computer vision, cognitive computing, artificial intelligence, ML lifecycle management and FPGA acceleration across various domains. We possess the capability to handle a complete Machine Learning (ML) pipeline involving dataset, model development, optimization, testing, and deployment. We also build ML transfer learning frameworks and AIML solutions on cloud as well as edge platforms.

Read our success stories related to artificial intelligence and machine learning services to know more about our expertise in the domain.

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Evolution of VLSI Technology and its Applications

VLSI Design & Verification /

The development of VLSI technology has opened up new possibilities in the field of microelectronics. The landscape of electronic systems has been fundamentally changed by VLSI technology, which can combine millions of transistors onto a single chip. This ground-breaking innovation has produced extremely advanced and effective electronic devices that are incredibly powerful and small. Research and Markets estimates that the global VLSI market will be worth USD 662.2 billion in 2023. Market analysts predict that it will be worth USD 971.71 billion in 2028, growing at an 8% Compound Annual Growth Rate (CAGR)

Several factors have influenced the evolution of VLSI technology, including advances in semiconductor materials and manufacturing processes, the development of computer-aided design (CAD) tools, and the growing demand for high-performance electronic systems which includes VLSI design and verification processes. In this article, we will explore the evolution of VLSI technology and its application in the modern world

Evolution of VLSI technology

The inception of VLSI technology can be traced back to the 1970s when the first microprocessor was introduced. A milestone that showcased the potential of VLSI design and integrating multiple transistors on a single chip. This breakthrough marked the beginning of a new era in microelectronics

A single chip can hold an ever-increasing number of transistors thanks to VLSI technology. The creation of transistors with smaller dimensions and better performance characteristics has been made possible by the development of semiconductor materials and manufacturing techniques. These advancements in VLSI design has caused an ongoing rise in integration density, allowing for the creation of extremely sophisticated and complex electronic systems. As the number of transistors integrated on a chip increases, the processing power of electronic systems also improves significantly. With more transistors available, complex computations can be executed at a faster rate, enabling high-performance computing. As a result, disciplines like artificial intelligence and machine learning, data analytics, and scientific simulations have advanced significantly

Applications of VLSI technology

VLSI technology has diverse application in various industries and sectors. Here are some key areas where VLSI plays a significant role

  • Consumer Electronics: VLSI technology has transformed the consumer electronics industry, enabling the development of smartphones, tablets, gaming consoles, and smartwatches. These devices offer advanced functionalities, high-speed processing, and energy efficiency, enhancing user experiences and productivity
  • Automotive Industry: In the automotive sector, VLSI technology has revolutionized vehicle functionality and safety. Advanced Driver Assistance Systems (ADAS), infotainment systems, and Engine Control Units (ECUs) utilize VLSI chips to enable features such as autonomous driving, object/lane/signal detection, and real-time vehicle diagnostics
  • Telecommunications: VLSI technology has played a vital role in the telecommunications industry. It has facilitated the development of high-speed network infrastructure, 5G wireless communication, and advanced mobile devices. VLSI-based chips are used in routers, modems, base stations, and network switches to enable fast and reliable data transmission
  • Healthcare: VLSI technology has had a significant impact on healthcare, enabling the development of medical imaging devices, wearable health monitors, and implantable medical devices. These devices provide accurate diagnostics, real-time monitoring, and improved patient care

Applications of VLSI technology

Advantages of VLSI technology

  • Compact size: VLSI circuits are much smaller than traditional circuits, enabling the development of compact electronic systems, thus making miniaturization possible
  • Lower power consumption: VLSI circuits consume less power compared to traditional circuits, making them more energy efficient. This is particularly relevant in applications where battery life is a critical factor, such as mobile devices
  • Higher performance: By integrating a large number of transistors on a single chip, VLSI circuits can perform complex operations at extremely fast speeds. This enables the development of high-performance electronic systems such as supercomputers, datacenters, edge computing, etc.
  • Mass production: VLSI technology has enabled the mass production of complex electronic systems. With the integration of multiple functions and components on one chip, by this reliability has improved. This, in turn, has made electronic systems more affordable and accessible to a wider range of users, promoting widespread adoption and innovation

Future of VLSI technology

VLSI technology’s future holds both opportunities and challenges. The need for evolving design methodologies that can handle the growing complexity of electronic systems is one of the challenges. Another difficulty is the growing need for energy-efficient systems, which necessitates the creation of fresh power management strategies

On the other hand, VLSI technology’s future presents several opportunities. VLSI technology has the potential to enable new applications and products, such as brain-machine interfaces and quantum computing. The increasing demand for high-performance electronic systems in various industries also presents opportunities for the development of new and innovative products and services

The development of VLSI technology has been fuelled by improvements in semiconductor materials, manufacturing techniques, and the rising demand for high-performance electronic systems. Applications in consumer electronics, automotive, telecommunications, healthcare, aerospace, and the Internet of Things (IoT) are just a few of the many domains where it is prevalent. As VLSI technology continues to advance, we can expect further innovations and breakthroughs that will shape the future of electronics and technology-driven industries

Softnautics offers a complete range of semiconductor design and verification services, catering to every stage of ASIC/FPGA/SoC development, from initial concept to final deployment. Our highly skilled VLSI team has the capability to design, develop, test, and verify customer solutions involving wide range of silicon platforms, tools and technology. Softnautics also have technology partnerships with leading semiconductor giants like Xilinx, Lattice Semiconductor and Microchip

Read our success stories related to VLSI design and verification services to know more about our expertise in the domain.

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QA Automation Testing with Container and Jenkins CICD

Quality Engineering /

Nowadays, Containers have become a leading CICD deployment technique. By employing appropriate connections with Source Code Management systems (SCM) like GIT, Jenkins is able to start a build procedure each time a developer contributes his code. This method makes all environments accessible to new Docker container images that are generated. Using these images, it allows for quicker application development, sharing, and deployment by groups. The Global CICD tool market is expected to see significant compound annual growth rate (CAGR) of 57.38% from 2023 to 2029, according to the report published by market intelligence data research.

Docker containers help developers in creating and conducting tests for their code in any circumstance to find flaws early in the life cycle of an application. It speeds up the process, reduces build time, and enable engineers to perform tests concurrently. Also, it can be integrated with tools like Jenkins and SCM platforms, e.g., GitHub. Developers upload their code to GitHub, test it using Jenkins, and then create an image utilizing that code. To address inconsistencies between various environment types, this image can be added to the Docker registry.

QA automation faces an issue when configuring Jenkins to execute automated testing within Docker containers and retrieve the results. The best approach for automating the testing procedure in CI/CD will be explored in this article ahead.

Continuous Integration (CI)

Each commit a developer makes to a code repository is automatically verified using a process called continuous integration. In most cases, validating the code entails constructing and testing it. Keep in mind that tests must be completed quickly. That is due to the developer’s need for prompt feedback on his changes. As a result, CI typically includes fake unit and/or integration tests.

Continuous Delivery (CD)

Continuous Delivery is a routine method for automatically releasing validated build artefacts.
After the code is built, integrated, tested, and passed, it is now appropriate to make build artefacts available. Naturally, a release must undergo testing before being deemed stable. As a result, we want to do release acceptance tests. Human and automated acceptance testing is available.
There are numerous considerations to remember:

  • Depending on the application, a MySQL database may be required
  • A method for determining whether automated tests are executed within a Success/Fail test cycle is required. Further stats may be needed
  • There must be a complete shutdown and removal of all containers built during a test run

Why to use containers?

Containers allow one to easily deploy code on multiple servers without purchasing additional hardware. Instead of purchasing two servers, you can deploy the code on a single server from a container. This reduces costs and makes scaling easier. Therefore, if a code only needs 1GB RAM but the server has 32GB, you waste the remaining 31GB of hardware. The idea of hardware virtualization was developed to prevent wastage.

However, there is still another waste: the code might not always use all RAM or CPU. Only 30% of the system’s resources will be used more than 90% of the time. Containerization was introduced to help with this as well. Containerization leverages underutilised system resources and shares hardware resources.

Jenkins automates the CI/CD process. The question arises as to where to do the continuous delivery? So, this problem of where to do delivery is solved by “container”, “server” or virtual machine.

Understanding the Container lifecycle

The base image is present in the container registry. An appropriate base image selection is the first step in docker life cycle. The required base image is selected from the container registry. Docker-file is written by a user to create a new image for the application using the base image. Add the required packages and application dependencies layered on the image. An account or own container registry is created by user for storing built images.

A new fresh image is created using docker-file and pushed to the container registry. With newly built image, run a container to test the application. Once the task is complete, stop and delete the container.

If any modifications are made in application inside the running container and the process demands all the modifications, then another image of the currently operating container is to be created. A new image is created with a commit command. This image is pushed to the container registry for further processing.

Container lifecycle

How the process works?

The initial step is to run a Docker container that will execute automated tests and deliver test results and an overall code completeness report.

Use the “dockerize” function to wait for an application to get started, if your tests rely on another service, such as a database, to be accessible.

Integrating dependencies

An application can be made up of many containers that run various services (e.g., application, related database). Manually starting and managing containers can be time-consuming, so docker developed a useful tool to help speed up the process: Docker Compose.

Performing the automated tests

It’s time to start a Jenkins project! Use the “Freestyle project” type. Add a build step labelled “Execute shell” after that.

For accurately referencing containers, it is essential to mention the project name to differentiate test containers from others on the same host.

We can monitor the log file results from container hosting tests to determine when tests have truly finished running.
Diagram

Automated testing with container in continuous delivery

The following are the benefits of using containers for automation and QA

  • Containerization improves SDLC effectiveness and speed, which benefits businesses
  • Because deployments are carried out in containers, it operates faster than Jenkins or any other tool. This makes it possible to deploy the same containers to various locations at once, which is incredibly quick as it leverages the Linux kernel
  • Due to the isolation provided by each container, maintaining tests and environments is simpler. The other containers won’t be impacted by a mistake in one of the containers
  • There is no requirement to pre-allocate RAM to containers because containers provide a predictable and repeatable testing environment. The host operating system or the device that hosts the Docker image runs the containers
  • The ability to keep the majority of application dependencies and configuration information inside a container reduces environmental variables. Continuous integration ensures application consistency during testing and production as it is done in parallel with testing
  • Running new applications or automation suites in new containers is simple
  • If a problem arises, testers can send photographs instead of bug reports to developers (in this case, an image of the programme, possibly even at the time a test failed). To address the issue, multiple developers can work simultaneously on debugging it. This is made possible by creating ‘n’ number of containers using the same image, allowing efficient collaboration among the development team. The duplication process can be easily performed
  • Early reports of glitches and problematic alterations
  • Automating repetitive manual test case execution
  • QA engineers can conduct more exploratory testing

Conclusion:

The adoption of containerized testing and the integration of Jenkins into the CI/CD process has revolutionized QA automation testing. By leveraging containers, developers can create and execute tests in any environment, leading to early flaw detection and faster application development.

Softnautics provides Quality Engineering Services for embedded software, device, product, and solution testing. This helps businesses create high-quality solutions that enable them to compete successfully in the market. Our comprehensive QE services include compliance, machine learning applications and platforms testing, embedded and product testing, DevOps and test automation. Additionally, we enable our solutions to adhere to numerous industry standards, including FuSa ISO 26262, MISRA C, AUTOSAR, and others.

Read our success stories related to Quality Engineering services to know more about our expertise in the domain.

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Role of Embedded System and its future in industrial automation

Platform Engineering /

Embedded systems have become increasingly important in today’s world of automation. Particularly in the field of industrial automation, embedded systems play an important role in speeding up production and controlling factory systems. In recent years, embedded systems have emerged as an essential component of various industries which is revolutionizing the automation of industrial processes. As a result of the integration of these systems into devices and machinery, manufacturing processes are streamlined, performance is enhanced, and efficiency is optimized. It is predicted in a survey report by market.us that the global embedded system market size will reach USD 173.4 billion by 2032, growing at a CAGR of 6.8% over the forecast period of 2023 to 2032. There is a growing demand for smart electronic devices, the Internet of Things (IoT), and automation across a number of industries that are driving this growth. Embedded systems in industrial automation will be discussed in this article, as well as some promising prospects.

What is the role of embedded systems and why are they essential in industrial automation?

A wide range of benefits and capabilities are provided by embedded systems based applications in the industry. As a result of its real-time control and monitoring capabilities, industries can optimize processes, make informed decisions, and respond swiftly to anomalies as they arise. The productivity and efficiency of embedded systems are improved by automating repetitive tasks and streamlining processes. The embedded system facilitates proactive monitoring, early hazard detection, and effective risk management with a focus on safety and risk management with their incorporation with cutting-edge technologies like AI, ML, and IoT. It also creates new opportunities for advanced analytics, proactive maintenance, and autonomous decision-making.

Roles of embedded system in industrial automation

Overall, the embedded system is an indispensable component of industrial automation, driving innovation, and enabling businesses to thrive in a dynamic and competitive landscape.

The industrial automation embedded system is divided into 2 main categories.

1. Machine control: Providing control over various equipment and processes is one of the main uses of embedded systems in industrial automation. With embedded systems serving as the central control point, manufacturing equipment, sensors, and devices can be precisely controlled and coordinated. To control the operation of motors, valves, actuators, and other components, these systems receive input from sensors, process the data, and produce output signals. Embedded systems make it possible for industrial processes to be carried out precisely and efficiently by managing and optimising the control systems. Let’s look at a few machine control use cases.

Manufacturing: Embedded systems are extensively used in manufacturing processes for machine control. They regulate and coordinate the operation of machinery, such as assembly lines, CNC machines, and industrial robots. Embedded systems ensure precise control over factors like speed, position, timing, and synchronization, enabling efficient and accurate production.

Robotics: Embedded systems play a critical role in controlling robotic systems. They govern the movements, actions, and interactions of robots in industries such as automotive manufacturing, warehouse logistics, and healthcare. Embedded systems enable robots to perform tasks like pick and place, welding, packaging, and inspection with high precision and reliability.

Energy management: Embedded systems are employed in energy management systems to monitor and control energy usage in industrial facilities. They regulate power distribution, manage energy consumption, and optimize energy usage based on demand and efficiency. Embedded systems enable businesses to track and analyze energy data, identify energy-saving opportunities, and implement energy conservation measures. These systems continuously monitor various energy consumption parameters, such as power usage, equipment efficiency, and operational patterns. By analysing the collected data, embedded systems can detect patterns and trends that indicate potential energy-saving opportunities.For example, they can identify instances of excessive energy consumption during specific periods or equipment malfunctions that result in energy waste. These insights enable businesses to optimize energy usage and reduce waste.

Classes of embedded system in industrial automation

2. Machine monitoring: Embedded systems are also utilized for monitoring in industrial automation. They are equipped with sensors and interfaces that enable the collection of real-time data from different points within the production environment. This data can include information about temperature, pressure, humidity, vibration, and other relevant parameters. Embedded systems process and analyze this data using machine learning and deep learning algorithms, providing valuable insights into the performance, status, and health of equipment and processes. By continuously monitoring critical variables, embedded systems facilitate predictive maintenance, early fault detection, and proactive decision-making, leading to improved reliability, reduced downtime, and enhanced operational efficiency. Some examples of machine monitoring:

Predictive maintenance: Intelligent embedded systems enable real-time monitoring of machine health and performance. By collecting data from sensors embedded within the machinery, these systems can analyze machine parameters such as temperature, vibration, and operating conditions. The collected data is utilized to identify irregularities and anticipate possible malfunctions, enabling proactive maintenance measures and minimizing unexpected downtime.

Quality control: Embedded systems in machine monitoring focus on product quality and consistency during manufacturing. They monitor variables such as pressure, speed, dimensions, and other relevant parameters to maintain consistent quality standards. For example, an embedded system may monitor pressure levels during the injection moulding process to ensure that the produced components meet the required specifications. If the pressure deviates from the acceptable range, the system can trigger an alarm or corrective action to rectify the issue. This will maintain the high standard of product quality.

Fault detection and safety: Machine monitoring systems detect potential faults or unsafe conditions in manufacturing environments. They continuously monitor machine performance and operating conditions to identify deviations from normal operating parameters. For instance, if an abnormal temperature rise is detected in a motor, indicating a potential fault or overheating, the embedded system can trigger an alarm or safety measure. This will prevent further damage or accidents. The focus here is on maintaining a safe working environment and protecting both equipment and personnel.

The Future of Embedded Systems in Industrial Automation
Embedded systems are poised to play a major role in industrial automation as automation demand continues to grow. These systems have the potential to improve efficiency, increase productivity, and drive innovation in industrial processes. Furthermore, the integration of embedded systems with emerging technologies like the Internet of Things (IoT) and Artificial Intelligence (AI) is expected to enhance their capabilities even further. Overall, embedded systems are essential for enabling businesses to thrive in the dynamic and competitive landscape of industrial automation.

Softnautics specializes in providing secure embedded systems, software development, and FPGA design services. We implement the best design practices and carefully select technology stacks to ensure optimal embedded solutions for our clients. Our platform engineering services include FPGA design, platform enablement, firmware and driver development, OS porting and bootloader optimization, middleware integration for embedded systems, and more. We have expertise across various platforms, allowing us to assist businesses in building next-generation systems, solutions, and products.

Read our success stories related to embedded system design to know more about our platform engineering services.

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Understanding the Deployment of Deep Learning Algorithms on Embedded Platforms

Understanding the Deployment of Deep Learning Algorithms on Embedded Platforms

AI Engineering /

Embedded platforms have become an integral part of our daily lives, revolutionizing our technological interaction. These platforms, equipped with deep learning algorithms, have opened a world of possibilities, enabling smart devices, autonomous systems, and intelligent applications. The deployment of deep learning algorithms on embedded platforms is crucial. It involves the process of optimizing and adapting deep learning models to run efficiently on resource-constrained embedded systems such as microcontrollers, FPGAs, and CPUs. This deployment process often requires model compression, quantization, and other techniques to reduce the model size and computational requirements without sacrificing performance.

The global market for embedded systems has experienced rapid expansion, expected to reach USD 170.04 billion in 2023. As per precedence research survey, it is expected to continue its upward trajectory, with estimates projecting it to reach approximately USD 258.6 billion by 2032. The forecasted Compound Annual Growth Rate (CAGR) during the period from 2023 to 2032 is around 4.77%. Several key insights emerge from the market analysis. In 2022, North America emerged as the dominant region, accounting for 51% of the total revenue share, while Asia Pacific held a considerable share of 24%. In terms of hardware platforms, the ASIC segment had a substantial market share of 31.5%, and the microprocessor segment captured 22.3% of the revenue share in 2022.

Embedded platforms have limited memory, processing power, and energy resources compared to traditional computing systems. Therefore, deploying deep learning algorithms on these platforms necessitates careful consideration of hardware constraints and trade-offs between accuracy and resource utilization.

The deployment includes converting the trained deep learning model into a format compatible with the target embedded platform. This involves converting the model to a framework-specific format or optimizing it for specific hardware accelerators or libraries.

Additionally, deploying deep learning algorithms on embedded platforms often involves leveraging hardware acceleration techniques such as GPU acceleration, specialized neural network accelerators, or custom hardware designs like FPGAs or ASICs. These hardware accelerators can significantly enhance the inference speed and energy efficiency of deep learning algorithms on embedded platforms. The deployment of deep learning algorithms on embedded platforms typically includes below.

Deep learning model deployment on various embedded platforms

Optimizing deep learning models for embedded deployment

Deploying deep learning algorithms on embedded platforms requires careful optimization and adaptation. Model compression, quantization, and pruning techniques help reduce the model’s size and computational requirements without compromising performance.

Hardware considerations for embedded deployment

Understanding the unique hardware constraints of embedded platforms is crucial for successful deployment. Factors such as available memory, processing power, and energy limitations needs to be carefully analysed. Selecting deep learning models and architectures that effectively utilize the resources of the target embedded platform is essential for optimal performance and efficiency.

Converting and adapting models for embedded systems

Converting trained deep learning models into formats compatible with embedded platforms is a critical step in the deployment process. Framework-specific formats such as TensorFlow Lite or ONNX are commonly used. Additionally, adapting models to leverage specialized hardware accelerators, like GPUs, neural network accelerators, or custom designs such as FPGAs or ASICs, can significantly enhance inference speed and energy efficiency on embedded platforms.

Real-time performance and latency constraints

In the domain of embedded systems, real-time performance and low latency are crucial. Deep learning algorithms must meet the timing requirements of specific applications, ensuring prompt and efficient execution of the inference process. Balancing real-time demands with the limited resources of embedded platforms requires careful optimization and fine-tuning.

If the deployed model doesn’t meet the desired performance or resource constraints, an iterative refinement process may be necessary. This could involve further model optimization, hardware tuning, or algorithmic changes to improve the performance or efficiency of the deployed deep learning algorithm.

Throughout the deployment process, it is important to consider factors such as real-time requirements, latency constraints, and the specific needs of the application to ensure that the deployed deep learning algorithm functions effectively on the embedded platform.

Frameworks and tools for deploying deep learning algorithms

Several frameworks and tools have emerged to facilitate the deployment of deep learning algorithms on embedded platforms. TensorFlow Lite, PyTorch Mobile, Caffe2, OpenVINO, and ARM CMSIS-NN library are among the popular choices, providing optimized libraries and runtime environments for efficient execution on embedded devices.

Let us see a few use cases where deep learning model deployment on embedded edge platforms is suitable.

  • Autonomous Vehicles: Autonomous vehicles rely heavily on computer vision algorithms trained using deep learning techniques such as Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs). These systems process images from cameras mounted on autonomous cars to detect objects like pedestrians crossing streets, parked cars along curbsides, cyclists riding, etc. based on which the autonomous vehicle perform actions.
  • Healthcare and Remote Monitoring: Healthcare: Deep learning is rapidly gaining traction in the healthcare industry. For instance, wearable sensors and devices utilize patient data to offer real-time insights into various health metrics, including overall health status, blood sugar levels, blood pressure, heart rate, and more. These technologies leverage deep learning algorithms to analyze and interpret the collected data, providing valuable information for monitoring and managing patient conditions.

Future trends and advancements

The future holds exciting advancements in deploying deep learning algorithms on embedded platforms, edge computing, with AI at the edge, enables real-time decision-making and reduced latency. The integration of deep learning with Internet of Things (IoT) devices further extends the possibilities of embedded AI. Custom hardware designs tailored for deep learning algorithms on embedded platforms are also anticipated, offering enhanced efficiency and performance.

Deploying deep learning algorithms on embedded platforms involves a structured process that optimizes models, considers hardware constraints, and addresses real-time performance requirements. By following this process, businesses can harness the power of AI on resource-constrained systems, driving innovation, streamlining operations, and delivering exceptional products and services. Embracing this technology empowers businesses to unlock new possibilities, leading to sustainable growth and success in today’s AI-driven world.

Furthermore, real-time performance requirements and latency constraints are critical considerations in deploying deep learning algorithms on embedded platforms, on which the efficient execution of the inference process depends.

At Softnautics, our team of AI/ML experts specializes in developing optimized Machine Learning solutions tailored for a wide range of edge platforms. Our expertise spans FPGA, ASIC, CPUs, GPUs, TPUs, and neural network compilers, ensuring efficient and high-performance implementations. Additionally, we also provide platform engineering services to design and develop secure embedded systems aligned with the best design methodologies and technology stacks. Whether it’s building cloud-based or edge-based AI/ML solutions, we are dedicated to helping businesses achieve exceptional performance and value.

Read our success stories related to Machine Learning expertise to know more about our services for accelerated AI solutions.

Contact us at business@softnautics.com for any queries related to your solution or for consultancy.

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An Outline of the Semiconductor Chip Design Flow

An Outline of the Semiconductor Chip Design Flow

VLSI Design & Verification /

Designing a chip is a complex and multi-step process that encompasses various stages, right from initial system specifications to manufacturing. Each step is crucial to achieving the ultimate objective of developing a fully functional chip. In this article, we will provide a brief overview of the chip design flow, its different stages and how they contribute to creating an effective chip. The stages include system specifications, architectural design, functional design, logic design, circuit design, physical design verification, and manufacturing.

The first step in any new development is to decide what kind of device/product will be designed, whether it is an integrated circuit (IC), ASIC, FPGA, SoC, etc. For example, if you want something small but powerful enough for high-speed applications such as telecommunications or networking equipment; then your best option would probably be an Application-Specific Integrated Circuit (ASIC). If you’re looking for something more flexible so that it can perform multiple tasks without much overhead; then maybe an FPGA would work better. Once this is decided, then the specifications can be defined.

Concept of chip design

A chip is a small electronic device that is programmed to perform a specific function. These devices are used in various applications, including computers and cell phones. VLSI technology has revolutionized the electronics industry by enabling designers to integrate millions or even billions of transistors onto a single chip. This has led to the development of powerful processors, memory devices, and other advanced electronic systems.

Chips are designed using different types of technology depending on their application requirements. Let us look into the flow of the entire chip design process.

System specification and architectural design
The first step in the chip design flow is to define the requirements and specifications of the chip. This includes defining what your product will do, how it will be used, and what performance metrics you need to meet. Once these requirements are defined, they can be used as input into designing your architecture and layout.

The next step in chip design after establishing the requirements is to create an architecture that meets them while keeping costs and power consumption to a minimum, among other considerations. During the initial phase of chip design, designers make crucial decisions about the architecture, such as choosing between RISC (Reduced Instruction Set Computer) or CISC (Complex Instruction Set Computer), determining the number of ALUs (Arithmetic Logic Units) required, deciding on the structure and number of pipelines, selecting cache size, and other factors. These choices form the foundation of the rest of the design process, so it is vital that designers carefully evaluate each aspect and consider how it will impact the chip’s overall efficiency and performance. These decisions are based on the chip’s intended use and defined requirements, with the ultimate goal of creating a design that is efficient and effective while minimizing power consumption and costs. After completing the architectural design phase, designers create a Micro-Architectural Specification (MAS), which is a written description of the chip’s architecture. This specification allows designers to accurately predict the design’s performance, power consumption, and die size. By creating a comprehensive MAS, designers can ensure that the chip meets the requirements and specifications established during the initial design phase. A thorough MAS is critical to avoid errors later in the process and to ensure that the chip design meets the required performance standards and timelines. This may involve choosing between different processor types or FPGAs (Field-Programmable Gate Arrays).

Chip Design Flow

Functional design
Next in the process is functional design. It involves defining the functionality and behavior of the chip. This includes creating a high-level description of the system’s requirements and designing the algorithms and data flow needed to meet those requirements. The goal of this stage is to create a functional specification that can be used as a blueprint for the rest of the design process.

Logic design
This step involves the creation of the digital logic circuits required to implement the functionality defined in the functional design stage. This stage includes creating a logical design using a hardware description language (HDL) and verifying the design’s correctness using simulations.

Circuit design
This stage involves designing the physical circuitry of the chip, including the selection of transistors, resistors, capacitors, and other components. The circuit design stage also involves designing the power supply and clock distribution networks for the chip.

Physical design verification
Physical design verification is the process of checking the physical layout of a chip. This involves identifying any design issues and ensuring that the chip will be manufactured correctly. In this step, design of integrated circuit layout is verified via EDA software tools like logic simulators, logic analyzers, etc. and various techniques such as Design Rule Check (DRC), Layout versus Schematic (LVS), and timing and power analysis to ensure correct electrical and logical functionality and manufacturability.

Verification and validation
Once you have completed the design of your chip, it is time to test it. This is called verification and validation (V&V). V&V involves testing the chip using various emulation and simulation platforms to ensure that it meets all the requirements and functions correctly. If there are any errors in the design, it will show up during this stage of development. Validation also helps identify the functional correctness of few initially manufactured prototypes.

At last is the fabrication of physical layout design. After the chip is designed and verified, a .GDS file is sent to foundry for fabrication.

Each stage of the chip design flow is critical to creating a successful and functional chip. By understanding the requirements of each stage, chip designers can create efficient, reliable, and cost-effective designs that meet the need of their customers across various industrial domains.

Future of chip design
The future of chip design is exciting and rapidly evolving, as technology advances. Next-gen chipsets enable new-age solutions by offering higher performance, lower power consumption, and increased functionality. These advancements drive innovation across many industries. One example of next-gen chipsets enabling new-age solutions is Artificial Intelligence (AI) and Machine Learning (ML) applications. AI and ML require significant computational power, which is possible with advanced chipsets. These technologies are used to create autonomous vehicles, personalized healthcare solutions, and advanced robotics, among others.

Another area where next-gen chipsets are making a significant impact is the Internet of Things (IoT) space. The proliferation of connected devices requires powerful, energy-efficient, and cost-effective chipsets to enable communication and data processing across a wide range of devices. Next-gen chipsets are also driving advancements in 5G networks, which are expected to deliver high-speed, low-latency connectivity and unlock new possibilities in areas such as virtual reality, augmented reality, and remote surgery.

The future of chip design is bright, and next-gen chipsets will enable more innovative solutions across many industries. As technology evolves, we can expect even more exciting developments in chip design and the solutions they enable.

To summarize, the chip design process is a complex one that involves many steps and stages. The impact of this on the industry is significant. There are many different types of chips in use today. With new technologies being developed all the time, there will always be room for improvement in terms of how we build these chipsets.

At Softnautics, with our semiconductor engineering services, we help silicon manufacturers in the chip design at any given stage by following best practices. We empower businesses with exceptional ASIC/FPGA platforms, tailored products and solutions, and highly optimized embedded systems. Our core competencies include RTL front-end design and integration, micro-architecture design, synthesis and optimization, IP/SoC level verification and pre/post validation. We also provide VLSI IPs for security, USB, and encryption.

Read our success stories related to the semiconductor industry to know more about our high-performance silicon services.

Contact us at business@softnautics.com for any queries related to your solution design or for consultancy.

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An Industrial Overview of Open Standards for Embedded Vision and Inferencing (2)

An Industrial Overview of Open Standards for Embedded Vision and Inferencing

Multimedia Engineering /

Embedded Vision and Inferencing are two critical technologies for many modern devices such as drones, autonomous cars, industrial robots, etc. Embedded vision uses computer vision to process images, video, and other visual data, while inferencing is the process of making decisions based on collected data without having to explicitly program each decision step with the help of diverse architectures and platforms including multi-core ARM, DSP, GPUs, and FPGAs to provide a comprehensive foundation for developing multimedia systems. Open standards are essential to the development of interoperable systems, and they allow for transparency in the development process while providing a level playing field for all participants.

The global market for embedded vision and inferencing was valued at USD 11.22 billion in 2021, with a compound annual growth rate (CAGR) of around 7% from 2022 to 2030. It’s worth noting that the market share for embedded vision and inferencing is likely to continue evolving rapidly as these technologies are becoming increasingly pivotal for next-gen solutions across industries.

Adoption of Open Standards

Open Standards have been widely adopted by the industry, with many companies adopting them as their default. The benefits of open standards are numerous and include:

  • Reduced cost for development and integration
  • Increased interoperability between systems and components
  • Reduced time to market

Open Standards for Embedded Vision

Open standards for embedded vision are a critical component of the Internet of Things (IoT). They provide a common language that allows devices to communicate with each other, regardless of manufacturer or operating system. Open standards for embedded vision include OpenCV, OpenVX, and OpenCL.

OpenCV is a popular open-source computer vision library that includes over 2,500 optimized algorithms for image and video analysis. It is used in applications such as object recognition, facial recognition, and motion tracking. OpenVX is an open-standard API for computer vision that enables performance and power-optimized implementation, reducing development time and cost. It provides a common set of high-level abstractions for applications to access hardware acceleration for vision processing. OpenCL is an open standard for parallel programming across CPUs, GPUs, and other processors that provides a unified programming model for developing high-performance applications. It enables developers to write code that can run on a variety of devices, making it a popular choice for embedded vision applications.

Open Standards for Embedded Vision & Inferencing

Open Standards for Inferencing

Open standards for inferencing are also essential for the development of intelligent systems. One of the most important open standards is the Open Neural Network Exchange (ONNX), which describes how deep learning models can be exported and imported between frameworks. It is currently supported by major frameworks like TensorFlow, PyTorch, and Caffe2.

ONNX enables interoperability between deep learning frameworks and inference engines, which is critical for the development of intelligent systems that can make decisions based on collected data. It provides a common format for representing deep learning models, making it easier for developers to build and deploy models across different platforms and devices.

Another important open standard for inferencing is the Neural Network Exchange Format (NNEF), which enables interoperability between deep learning frameworks and inference engines. It provides a common format for deploying and executing trained neural networks on various devices. It allows developers to build models using their framework of choice and deploy them to a variety of devices, making it easier to build intelligent systems that can make decisions based on collected data.

Future of Open Standards

The future of open standards for embedded vision and inferencing is bright, but there are challenges ahead. One of the biggest challenges is the lack of support for open-source software in embedded systems. This means that open-source libraries and frameworks cannot be used on devices with limited memory and processing power. Another challenge is the wide variety of processors and operating systems available, making it difficult to create a standard that works across all devices. However, there are initiatives underway like hardware innovation and algorithm optimization to address these challenges.

Industry Impact of Open Standards

Open standards have a significant impact on the industry and consumers. The use of open standards has enabled an ecosystem to develop around machine learning and deep learning algorithms, which is essential for innovation in this space. Open-source software has been instrumental in accelerating the adoption of AI across industries, including automotive, consumer, financial services, manufacturing, and healthcare.

Open standards also have a direct impact on consumers by lowering costs, increasing interoperability, and improving security across devices. For example, standards enable companies to build products using fewer components than proprietary solutions require, reducing costs for manufacturers and end-users who purchase products with embedded vision technology built-in (e.g., cameras).

Open Standards are the key to unlocking the full potential of embedded vision and inferencing. They allow developers to focus on their applications, rather than on the underlying hardware or software platforms. Open standards also provide a level playing field for all types of companies – from startup to large enterprises – to compete in this growing market. Overall, open standards are crucial for unlocking the full potential of embedded vision and inferencing for designing next-gen solutions across various industries.

At Softnautics, we help business across various industries to design embedded multimedia solutions based on next-gen technologies like computer vision, deep learning, cognitive computing and more. We also have hands-on experience in designing high-performance media applications, architect complete video pipelines, audio/video codecs engineering, applications porting, ML model design, train, optimize, test, and deploy.

Read our success stories related to intelligent media solutions to know more about our multimedia engineering services.

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Pytest advanced features

Utilization of Advanced Pytest Features

Quality Engineering /

In the world of extreme technology advancements, each product, system or platform is completely different from another. Hence, the testing needs too vary drastically.
To test a web application, the system needs are limited to just a web browser client and Selenium for test automation. Whereas an IoT product needs end device, cloud, web app, API as well as Mobile application (Android & iOS) to be tested thoroughly end to end.
Similarly, there can be a completely different type of product or system. So, one size fits all is never an option when it comes to testing. To test different types of systems, one needs to be extremely versatile as well as flexible. Same goes for test automation. Each system needs a completely different type of automation framework to accommodate the complexity, scalability and different components of the product under test. If there is a single test framework which is versatile and flexible enough to achieve all of the above, it is Pytest.

Pytest provides numerous advantages that can assist companies in optimizing their software testing procedures and enhancing the overall quality of their products. One of the key benefits of Pytest is its seamless integration with continuous integration and delivery (CI/CD) pipelines, which allows for automated testing of code modifications in real time. This results in improved efficiency and faster bug resolution time, ensuring that the product meets the desired level of quality and reliability. Pytest offers comprehensive reporting and analysis functionalities, which can help developers and testers promptly identify and resolve issues.

Pytest offers a huge number of Features in form of Functions, Markers, Hooks, Objects, Configurations and a lot more to make the framework development extremely flexible and give freedom to the test framework architect to implement the desired structure, flow and outcome which best fits product requirement.

But to make use of the above, and what to use when, is a major challenge. This blog will explain the features of different categories which are used to achieve complex automation.

Hooks:
Hooks are a key part of Pytest’s plugin system and are used by plugins and by Pytest itself to extend the functionality of Pytest. Hooks allow plugins to register custom code to be run at specific points during the execution of Pytest, such as before and after tests are run, or when exceptions are raised. They provide a flexible way to customize the behavior of Pytest and to extend its functionality. The categories of hooks go by the stage at which they are used.
Some widely used hooks in each category summarized below:

BootStrapping: At the very beginning and end of test run.

  • pytest_load_initial_conftests – Load initial plugins and modules that are needed to configure and setup the test run ahead of the command-line argument parsing.

Initialization: After boot strapping to initialize the resources needed for test run.

  • pytest_addoption – add additional command line options to the test runner.
  • pytest_configure – Perform additional setup/configuration after command line options are parsed.
  • pytest_sessionstart – Perform setup steps after all configurations are completed and before the test session starts.
  • pytest_sessionfinish – Teardown steps or generate reports after all tests are run.

Collection: During test collection process, used to create custom test suites and collect test items.

    • pytest_collectstart – Perform steps/action before the collection starts.
    • pytest_ignore_collect – Ignore the collection for specified path.
    • pytest_generate_tests – Generate multiple parameterized calls for a test based on parameter.
    • pytest_collection_modifyitems – Modify the collected test items list as needed. Filter, Re-order according to markers, or other criteria.

Runtest: Control and customize individual test run

  • pytest_runtest_setup/teardown – Setup/teardown for a test run.
  • pytest_runtest_call – Modify the arguments or customize the test when a test is called.
  • pytest_runtest_logreport – Access the test result and modify/format before it is logged.

Reporting: Report status of test run and customize reporting of test results.

  • pytest_report_header – Add additional information to the test report.
  • pytest_terminal_summary – Modify/Add details to terminal summary of the test results.

Debugging/Interaction: Interact with the test run that is in progress and debug issues.

  • pytest_keyboard_interrupt – Perform some action on keyboard interrupt.
  • pytest_exception_interact – Called when an exception is raised which can be interactively be handled.
  • pytest_enter/leave_pdb – Action to perform when python debugger enters/leaves interactive mode.

Functions:
As the name suggests, these are independent pytest functions to perform a specific operation/task.
Pytest functions are directly called like a regular python function call. i.e. pytest.()
There are a number of Pytest functions available to perform different operations.
Below listed are widely used Pytest functions and their uses.

approx: assert that two numbers are equal to each other with some tolerance.
Example:
assert 2.2 == pytest.approx(2.3)
# fails, because 2.2 is not similar to 2.3
assert 2.2 == pytest.approx(2.3, 0.1)
#pass with 0.1 tolerance

skip: skip an executing test with given message reason. Used to skip on encountering a certain condition.
Example:
import pytest
if condition_is_encountered:
pytest.skip(“Skip integration test”)

fail: Explicitly fail an executing test with given message. Usually used to explicitly fail test while handling an exception.
Example:
import pytest
a = [1, 2, 3]
try:
invalid_index = a[3]
except Exception as e:
pytest.fail(f”Failing test due to exception: {e}”)

xfail: Explicitly fail a test. Used for known bugs. Alternatively preferable to use pytest.mark.xfail.
Example:
pytest.xfail(“This is an existing bug”)

skip: Skip the test with a given message.
Example:
pytest.skip(“Required environment variables were not set, integration tests
will be skipped”)

raises: Validate the expected exception is raised by a particular block of code under the context manager.

Example:
with pytest.raises(ZeroDivisionError):
1 / 0

Importorskip: Import a module or skip the test if module import fails.
Example:
pytest.importorskip(‘graphviz’)

Marks:
Marks can be used to apply meta data to test functions (but not fixtures), which can then be accessed by fixtures or plugins. They are very commonly used for test parameterization, test filtering, skipping and adding other metadata. Marks are used as decorators.

@pytest.mark.parametrize: Parametrization of arguments for a test function. The collection will generate multiple instances of the same test function as the number of parameters.
Example:
@pytest.mark.parametrize(“test_input,expected”, [(“3+5”, 8), (“2+4”, 6), (“6*9”, 42)])
def test_eval(test_input, expected):
assert eval(test_input) == expected

@pytest.mark.usefixtures: A very useful marker to define which fixture or fixtures to use for the underlying test function. The fixture names can be specified as a comma separated list of strings.
Example:
@pytest.mark.usefixtures(‘fixture_one_name’, ‘fixture_two_name’)

@pytest.mark.custom_markers: These are markers that are created dynamically which user can give name as per the requirement. These custom markers are mainly used to test filtering and categorizing different sets/types of tests.
Example:
@pytest.mark.timeout(10, “slow”, method=”thread”)
@pytest.mark.slow
def test_function():
…

Conclusion:
Pytest is way above and over the above listed concepts. Compiled the most useful concepts that are difficult to find all at one place. These concepts are useful for a framework developer to plan the architecture of the test automation framework and make the most of them to build an efficient, flexible, robust and scalable test automation framework.

Softnautics offers top-notch Quality Engineering Services for software and embedded devices, enabling businesses to develop high-quality solutions that are well-suited for the competitive marketplace. Testing of embedded software and devices, DevOps and test automation, as well as machine learning application and platform testing, are all part of our Quality Engineering services. Our team of experts have experience working on automation frameworks and tools like Jenkins, Python, Robot, Selenium, JIRA, TestRail, Jmeter, Git and more, as well as ensure compliance with industrial standards such as FuSa – ISO 26262, MISRA C, and AUTOSAR.

To streamline the testing process, we have developed STAF, our in-house test automation framework that facilitates end-to-end product/solution testing with greater efficiency and faster time-to-market. Softnautics comprehensive Quality Engineering services have a proven track record of success, as evident in our numerous success stories across different domains.

For any queries related to your solution design or require consultation, please contact us at business@softnautics.com.

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