Artificial Intelligence and machine learning has significantly revolutionized the Advanced Driver Assistance System (ADAS), by utilizing the strength of deep learning techniques. ADAS relies heavily on deep learning to analyze and interpret large amounts of data obtained from a wide range of sensors. Cameras, LiDAR (Light Detection and Ranging), radar, and ultrasonic sensors are examples of these sensors. The data collected in real-time from the surrounding environment of the vehicle encompasses images, video, and sensor readings.

By effectively incorporating machine learning development techniques into the training deep learning models, ADAS systems can analyze the sensor data in real-time and make informed decisions to enhance driver safety and assist in driving tasks, making it future ready for autonomous driving. They can also estimate distances, velocities, and trajectories of surrounding objects, allowing ADAS systems to predict potential collisions and provide timely warnings or take preventive actions. Let’s dive into the key steps of deep learning techniques in the Advanced Driver Assistance System and tools commonly used in the development and deployment of ADAS systems.

Key steps in the development and deployment of deep learning models for ADAS

Data preprocessing

Data preprocessing in ADAS focuses on preparing collected data for effective analysis and decision-making. It involves tasks such as cleaning data to remove errors and inconsistencies, handling missing values through interpolation or extrapolation, addressing outliers, and normalizing features. For image data, resizing ensures consistency, while normalization methods standardize pixel values. Sensor data, such as LiDAR or radar readings, may undergo filtering techniques like noise removal or outlier detection to enhance quality.

By performing these preprocessing steps, the ADAS system can work with reliable and standardized data, improving the accuracy of predictions and overall system performance.

Network architecture selection

Network architecture selection is another important process in ADAS as it optimizes performance, ensures computational efficiency, balances model complexity, and interpretability, enables generalization to diverse scenarios, and adapts to hardware constraints. By choosing appropriate architectures, such as Convolutional Neural Networks (CNNs) for visual tasks and Recurrent Neural Networks (RNNs) or Long Short-Term Memory Networks (LSTM) for sequential data analysis, ADAS systems can improve accuracy, achieve real-time processing, interpret model decisions, and effectively handle various driving conditions while operating within resource limitations. CNNs utilize convolutional and pooling layers to process images and capture spatial characteristics, while RNNs and LSTMs capture temporal dependencies and retain memory for tasks like predicting driver behavior or detecting drowsiness.

Training data preparation

Training data preparation in ADAS helps in data splitting, data augmentation, and other necessary steps to ensure effective model learning and performance. Data splitting involves dividing the collected datasets into training, validation, and testing sets, enabling the deep learning network to be trained, hyperparameters to be tuned using the validation set, and the final model’s performance to be evaluated using the testing set.

Data augmentation techniques, such as flipping, rotating, or adding noise to images, are employed to enhance the diversity and size of the training data, mitigating the risk of overfitting. These steps collectively enhance the quality, diversity, and reliability of the training data, enabling the ADAS system to make accurate and robust decisions.

Network Architectures and Autonomous Features in ADAS

Training process

The training process in an ADAS system involves training deep learning models using optimization algorithms and loss functions. These methods are employed to optimize the model’s performance, minimize errors, and enable accurate predictions in real-world driving scenarios. By adjusting the model’s parameters through the optimization process, the model learns from data and improves its ability to make informed decisions, enhancing the overall effectiveness of the ADAS system.

Object detection and tracking

Object detection and tracking is also a crucial step in ADAS as it enables systems to detect the driving lanes or implement pedestrian detection to improve road safety. There are several techniques to perform object detection in ADAS, some popular deep learning-based techniques are Region-based Convolutional Neural Networks (R-CNN), Single Shot MultiBox Detector (SSD) and You Only Look Once (YOLO).

Deployment

The deployment of deep learning models in ADAS ensure that the trained deep learning models are compatible with the vehicle’s hardware components, such as an onboard computer or specialized processors. The model must be adapted so that it can function seamlessly within the hardware architecture that already exists. The models need to be integrated into the vehicle’s software stack, allowing them to communicate with other software modules and sensors. They process real-time sensor data from various sources, such as cameras, LiDAR, radar, and ultrasonic sensors. These deployed models analyze incoming data streams, detect objects, identify lane markings, and make driving-related decisions based on their interpretations. This real-time processing is crucial for providing timely warnings and assisting drivers in critical situations.

Continuous learning and updating

• Online learning: The ADAS system can be designed to continually learn and update the deep learning models based on new data and experiences. This involves incorporating mechanisms to adapt the models to changing driving conditions, new scenarios, and evolving safety requirements.
• Data collection and annotation: Continuous learning requires the collection of new data and annotations to train updated models. This may involve data acquisition from various sensors, manual annotation or labeling of the collected data, and updating the training pipeline accordingly.
• Model re-training and fine-tuning: When new data is collected, the existing deep learning models can be re-trained or fine-tuned using the new data to adapt to emerging patterns or changes in the driving environment.

Now let us see commonly used tools, frameworks and libraries in ADAS development.

• TensorFlow: An open-source deep learning framework developed by Google. It provides a comprehensive ecosystem for building and training neural networks, including tools for data pre-processing, network construction, and model deployment.
• PyTorch: Another widely used open-source deep learning framework that offers dynamics computational graphs, making it suitable for research and prototyping. It provides a range of tools and utilities for building and training deep learning models.
• Keras: A high-level deep learning library that runs on top of TensorFlow. It offers a user-friendly interface for building and training neural networks, making it accessible for beginners and rapid prototyping.
• Caffe: A deep learning framework specifically designed for speed and efficiency, often used for real-time applications in ADAS. It provides a rich set of pre-trained models and tools for model deployment.
• OpenCV: A popular computer vision library that offers a wide range of image and video processing functions. It is frequently used for pre-processing sensor data, performing image transformations, and implementing computer vision algorithms in ADAS applications.

To summarize, the integration of deep learning techniques into ADAS systems empowers them to analyze and interpret real-time data from various sensors, enabling accurate object detection, collision prediction, and proactive decision-making. This ultimately contributes to safer and more advanced driving assistance capabilities.

At Softnautics, a MosChip company, our team of AIML experts are dedicated to developing optimized Machine Learning solutions tailored for diverse techniques in deep learning. Our expertise covers deployment on cloud, edge platforms like FPGA, ASIC, CPUs, GPUs, TPUs, and neural network compilers, ensuring the implementation of efficient and high-performance artificial intelligence and 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 expertise to know more about our AI engineering services.

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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.

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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.

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

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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.

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A Machine Learning (ML) pipeline is used to assist in the automation of machine learning processes. They work by allowing a sequence of data to be transformed and correlated in a model that can be tested and evaluated to achieve a positive or negative outcome. Starting from data extraction and pre-processing to model training & tuning, analysis of the model and deployment would run in a single entity in mainstream design. This means that the data will be extracted, cleaned, prepared, modelled, and deployed using the same script. Because machine learning models typically contain far less code than other software applications, keeping all resources in one place makes perfect sense. Because of advancements in deep learning, and neural network algorithms, the global market is expected to gain traction. Furthermore, many companies are tightening their deep learning capabilities to drive innovation, which is expected to drive ML market growth across industries like automotive, consumer electronics, media & entertainment, and others. According to the precedence research group, the global ML as a service market was valued at USD 15.47 billion in 2021 and it is predicted to reach USD 305.62 billion by 2030, with a CAGR of 39.3 percent from 2022 to 2030. 

Overview of machine learning pipeline

A machine learning pipeline is a method for fully automating a machine learning task’s workflow. This can be accomplished by allowing a series of data to be converted and associated in a model that can be examined to determine the output. A general ML pipeline consists of data input, data models, parameters, and predicted outcomes. The process of creating a machine learning model can be codified and automated using a machine learning pipeline. The deployment of various versions of the same model, model expansion, and workflow setup difficulties may arise while executing the ML process and must be handled manually. We can utilize a machine learning pipeline to address all of the aforementioned issues. Each step of the workflow functions independently using the ML pipeline. Therefore, one may select that module and use it as needed for any updates at any stage.

Overview of ML Pipeline

Data input
The Data input step is the first step in every ML pipeline. The data is organized and processed in this stage so that it can be applied to subsequent steps. 

Image processing is the process of converting an image to a digital format and then performing various operations on it to gather useful information. Artificial Intelligence (AI) and Machine Learning (ML) has had a huge influence on various fields of technology in recent years. Computer vision, the ability for computers to understand images and videos on their own, is one of the top trends in this industry. The popularity of computer vision is growing like never before and its application is spanning across industries like automobiles, consumer electronics, retail, manufacturing and many more. Image processing can be done in two ways: Physical photographs, printouts, and other hard copies of images being processed using analogue image processing and digital image processing is the use of computer algorithms to manipulate digital images. The input in both cases is an image. The output of analogue image processing is always an image. However, the output of digital image processing may be an image or information associated with that image, such as data on features, attributes, and bounding boxes. According to a report published by Data Bridge Market Research analyses, the Image processing systems market is expected to grow at a CAGR of 21.8% registering a market value of USD 151,632.6 million by 2029. Image processing is used in a variety of use cases today, including visualisation, pattern recognition, segmentation, image information extraction, classification, and many others.

Image processing working mechanism

Artificial intelligence and Machine Learning algorithms usually use a workflow to learn from data. Consider a generic model of a working algorithm for an Image Processing use case. To start, AI algorithms require a large amount of high-quality data to learn and predict highly accurate results. As a result, we must ensure that the images are well-processed, annotated, and generic for AIML image processing. This is where computer vision (CV) comes in; it is a field concerned with machines understanding image data. We can use CV to process, load, transform, and manipulate images to create an ideal dataset for the AI algorithm.
Let’s understand the workflow of a basic image processing system

An Overview of Image Processing System

Acquisition of image
The initial level begins with image pre-processing which uses a sensor to capture the image and transform it into a usable format.

Enhancement of image
Image enhancement is the technique of bringing out and emphasising specific interesting characteristics which are hidden in an image.

Restoration of image
Image restoration is the process of enhancing an image’s look. Picture restoration, as opposed to image augmentation, is carried out utilising specific mathematical or probabilistic models.

Colour image processing
A variety of digital colour modelling approaches such as HSI (Hue-Saturation-Intensity), CMY (Cyan-Magenta-Yellow) and RGB (Red-Green-Blue) etc. are used in colour picture processing.

Compression and decompression of image
This enables adjustments to image resolution and size, whether for image reduction or restoration depending on the situation, without lowering image quality below a desirable level. Lossy and lossless compression techniques are the two main types of image file compression which are being employed in this stage.

Morphological processing
Digital images are processed depending on their shapes using an image processing technique known as morphological operations. The operations depend on the pixel values rather than their numerical values, and well suited for the processing of binary images. It aids in removing imperfections for structure of the image.

Segmentation, representation and description
The segmentation process divides a picture into segments, and each segment is represented and described in such a way that it can be processed further by a computer. The image’s quality and regional characteristics are covered by representation. The description’s job is to extract quantitative data that helps distinguish one class of items from another.

Recognition of image
A label is given to an object through recognition based on its description. Some of the often-employed algorithms in the process of recognising images include the Scale-invariant Feature Transform (SIFT), the Speeded Up Robust Features (SURF), and the PCA (Principal Component Analysis).

Frameworks for AI image processing

Open CV
OpenCV is a well-known computer vision library that provides numerous algorithms and utilities to support the algorithms. The modules for object detection, machine learning, and image processing are only a few of the many that it includes. With the help of this programme, you may do picture processing tasks like data extraction, restoration, and compression.

TensorFlow
TensorFlow, created by Google, is one of the most well-known end-to-end machine learning programming frameworks for tackling the challenges of building and training a neural network to automatically locate and categorise images to a level of human perception. It offers functionalities like work on multiple parallel processors, cross platform, GPU configuration, support for a wide range of neural network algorithms, etc.

PyTorch
Intended to shorten the time it takes to get from a research prototype to commercial development, it includes features like a tool and library ecosystem, support for popular cloud platforms, a simple transition from development to production, distribution training, etc.

Caffe
It is a deep learning framework intended for image classification and segmentation. It has features like simple CPU and GPU switching, optimised model definition and configuration, computation utilising blobs, etc.

Applications

Machine vision
The ability of a computer to comprehend the world is known as machine vision. Digital signal processing and analogue-to-digital conversion are combined with one or more video cameras. The image data is transmitted to a robot controller or computer. This technology aids companies in improving automated processes through automated analysis. For instance, specialised machine vision image processing methods can frequently sort parts more efficiently when tactile methods are insufficient for robotic systems to sort through various shapes and sizes of parts. These methods use very specific algorithms that consider the parameters of the colours or greyscale values in the image to accurately define outlines or sizing for an object.

Pattern recognition
The technique of identifying patterns with the aid of a machine learning system is called pattern recognition. The classification of data generally takes place based on previously acquired knowledge or statistical data extrapolated from patterns and/or their representation. Image processing is used in pattern recognition to identify the items in an image, and machine learning is then used to train the system to recognise changes in patterns. Pattern recognition is utilised in computer assisted diagnosis, handwriting recognition, image identification, character recognition etc.

Digital video processing
A video is nothing more than just a series of images that move quickly. The number of frames or photos per minute and the calibre of each frame employed determine the video’s quality. Noise reduction, detail improvement, motion detection, frame rate conversion, aspect ratio conversion, colour space conversion, etc. are all aspects of video processing. Televisions, VCRs, DVD players, video codecs, and other devices all use video processing techniques.

Transmission and encoding
Today, thanks to technological advancements, we can instantly view live CCTV footage or video feeds from anywhere in the world. This indicates that image transmission and encoding have both advanced significantly. Progressive image transmission is a technique of encoding and decoding digital information representing an image in a way that the image’s main features, like outlines, can be presented at low resolution initially and then refined to greater resolutions. An image is encoded by an electronic analogue to multiple scans of the exact image at different resolutions in progressive transmission. Progressive image decoding results in a preliminary approximate reconstruction of the image, followed by successively better images whose adherence is gradually built up from succeeding scan results at the receiver side. Additionally, image compression reduces the amount of data needed to describe a digital image by eliminating extra data, ensuring that the image processing is finished and that it is suitable for transmission.

Image sharpening and restoration
The technique of identifying patterns with the aid of a machine learning system is called pattern recognition. The classification of data generally takes place based on previously acquired knowledge or statistical data extrapolated from patterns and/or their representation. Image processing is used in pattern recognition to identify the items in an image, and machine learning is then used to train the system to recognise changes in patterns. Pattern recognition is utilised in computer assisted diagnosis, handwriting recognition, image identification, character recognition etc.