An overview of Machine Learning pipeline and its importance

An overview of Machine Learning pipeline and its importance

AI Engineering /

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. 

Validation of data
Data validation is the next step, which must be completed before training a new model. The statistics of the new data, such as the scope, number of classifications, distribution of subgroups, etc., are the main focus of data validation. We can compare various datasets to find anomalies using a variety of data validation tools like Python, R, Python Pandas, etc.

Pre-processing of data 
One of the most important phases of each ML lifecycle as well as the pipeline is data pre-processing. As it might produce a sudden and unexpected result, we cannot input the collected data directly to train the model without first processing it. The pre-processing stage entails getting the raw data ready for the ML model. The procedure is divided into several parts, such as attribute scaling, data cleansing, information quality assessment, and data reduction. The final dataset that can be utilised for model training and testing is the result of the data pre-processing procedure. In machine learning, a variety of methods like normalization, aggregation, numerosity reduction, etc. are available for pre-processing data.

Data model training
Each ML pipeline’s central step is model training. In this step, the model is trained to predict the output as accurately as possible given the input (a pre-processed dataset). Larger models or training data sets, however, might present some challenges. Therefore, efficient model training or model tuning distribution is needed for this. Because pipelines are scalable and can process many models at once, they can address the problem of the model training stage. Different sorts of ML algorithms like Supervised, Unsupervised, and Reinforcement learnings can be utilized for building data models. 

Deployment of model
It’s time to deploy the model after training and analysis. Three methods exist for deploying ML models: through the model server, a browser, and an edge device. However, employing a model server is the typical method of deployment for the model. ML pipeline ensures smooth functioning of ML inference at edge level devices where the data generation plays a crucial part and offers features like lower cost, real time processing, and increased privacy. And for cloud services, the ML pipeline ensures proper utilization of resource demand and reduces processing power and consumes fewer data storage space. The ability to host different versions concurrently on model servers makes it possible to do A/B tests on models and can yield insightful feedback for model improvement.

Benefits of a machine learning pipeline include.

  • Providing a comprehensive view of the entire series of phases by mapping a complex process that incorporates input from various specialties.
  • Concentrating on particular steps in the sequence one at a time allows for the automation of individual phases. It is possible to integrate machine learning pipelines, increasing productivity and automating processes.
  • It offers the flexibility to easily debug the entire code and trace out the issues in a particular step.
  • Easily deployable, upscaling modular machine learning pipeline components as necessary.
  • Offers the flexibility of using multiple pipelines which are reliably coordinated over heterogeneous system resources as well as different storage locations.

Each machine learning pipeline will be slightly different depending on the model’s use case and the organization using it. However, since the pipeline frequently adheres to a typical machine learning lifecycle, the same factors must be taken into account when developing any machine learning pipeline. Consider the various phases of machine learning and divide each phase into distinct modules as the first step in the process. A modular approach facilitates the gradual enhancement of each component of the machine learning pipeline and makes it easier to concentrate on the individual parts of the pipeline.

Softnautics with its AI engineering and machine learning services helps businesses build intelligent solutions in the areas of computer vision, cognitive computing, artificial intelligence & FPGA acceleration. We possess the capability to handle a complete Machine Learning (ML) pipeline involving dataset, model development, optimization, testing, and deployment. We collaborate with organizations to develop high-performance cloud-to-edge machine learning solutions like face/gesture recognition, people counting, object/lane detection, weapon detection, food classification, and more across a variety of platforms.

Read our success stories related to Machine Learning services and AI engineering solution design to know more about our expertise for the domain.

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FPGAs and GPUs for AI Based Applications

Selection of FPGAs and GPUs for AI Based Applications

AI Engineering, Platform Engineering /

Artificial Intelligence (AI) refers to non-human, machine intelligence capable of making decisions in the same way that humans do. This includes contemplation, adaptability, intention faculties, and judgment. Machine vision, robotic automation, cognitive computing, machine learning, and computer vision are all applications in the AI market. AI is rapidly gaining traction in a variety of industry sectors like automotive, consumer electronics, media & entertainment, and semiconductors, heralding the next great technological shift. The scope for semiconductor manufactures is expected to grow in the coming years. As the demand for machine learning devices grow around the world, many major market players belonging to EDA (Electronic Design Automation), graphics cards, gaming, multimedia industries are investing to provide innovative and high-speed computing processors. While AI is primarily based on software algorithms that mimic human thoughts and ideas, hardware is also an important component. Field Programmable Gate Arrays (FPGAs) and Graphics Processing Units (GPUs) are the two main hardware solutions for most AI operations. According to the precedence research group, the global AI in hardware market was valued at USD 10.41 billion in 2021 and it is predicted to reach USD 89.22 billion by 2030, with a CAGR of 26.96 percent from 2022 to 2030.

FPGA vs GPU

Overview of FPGA
A hardware circuit with reprogrammable logic gates is known as a field-programmable gate array (FPGA). While a chip is being used in the field, users can design a unique circuit by overwriting the configurations. This contrasts with standard chips, which cannot be reprogrammed. With an FPGA chip, you can build anything from simple logic gates to multi-core chipsets. The usage of FPGAs is very much popular where intrinsic circuitry is essential, and changes are expected. ASIC prototyping, automotive, multimedia, consumer electronics, and many more areas are covered by FPGA applications. Based on the application requirement, either low-end, mid-range, or high-end FPGA configurations are selected. ECP3 and ECP5 series from Lattice semiconductor, Artix-7/Kintex-7 series from Xilinx, and Stratix family from Intel are some of the popular FPGA designs for low power & low design density.

The logic blocks are built using look-up tables (LUTs) with a limited of inputs and are built using basic memory such as SRAM or Flash to store Boolean functions. Each LUT is linked to a multiplexer and a flip-flop register to support sequential circuits. Similarly, many LUTs can be used to create complex functions. Read our FPGA blog to know more about its architecture.

FPGAs are more suitable for embedded applications and use less power than CPUs and GPUs. These circuits are not constrained by design like GPUs and can be used with bespoke data types. Additionally, FPGAs’ programmability makes it simpler to modify them to address security and safety issues.

Advantages of using FPGAs
Energy efficient
Designers can precisely adjust the hardware to meet the requirements of the application, thanks to FPGAs. With its low power consuming capability, overall power consumption for AI and ML applications can be minimized. This could increase the equipment’s lifespan and reduce the training’s overall cost.

Ease of flexibility
FPGA offers the flexibility of programmability for handling AI/ML applications. One can program one individual block or an entire block depending on the requirements.

Reduced latency
FPGAs excel at handling short batch phrases with reduced latency. Reduced latency refers to a computing system’s ability to respond with minimal delay. This is critical in real-time data processing applications such as video surveillance, video pre and post processing, and text recognition, where every microsecond counts. Because they operate in a bare-metal environment without an operating system, FPGAs and ASICs are faster than GPUs.

Parallel processing
The operational and energy efficiency of FPGAs is substantially improved by their ability to host several tasks concurrently and even designate specific sections of the device for particular functions. Small quantities of distributed memory are included in the fabric of the FPGAs’ special architecture, bringing them closer to the processor.

Overview of GPU
The original purpose of graphic processing units (GPUs) was to create computer graphics, and virtual reality environments that depended on complex computations and floating-point capabilities to render geometric objects. A modern artificial intelligence infrastructure would not be complete without them and are very much suitable for the deep learning process.

Artificial intelligence needs a lot of data to study and learn from to be successful. To run AI algorithms and move a lot of data, demands a lot of computational power. GPUs can carry out these tasks because they were created to quickly handle the massive volumes of data required for generating graphics and video. Their widespread use in machine learning and artificial intelligence applications is due in part to their high computing capabilities.

GPUs can handle several computations at once. As a result, training procedures can be distributed, which greatly speeds up machine learning activities. With GPUs, you may add several cores with lower resource requirements without compromising performance or power. Various types of GPUs are available in the market and generally fall into the following categories such as data center GPUs, consumer grade GPUs, and enterprise grade GPUs.

Advantages of using GPUs
Memory bandwidth
GPUs have good memory bandwidth due to which they tend to perform computation quickly in the case of deep learning applications. GPUs consume less memory when training the model on huge datasets. With up to 750GB of memory bandwidth, they can really accelerate quick processing of AI algorithms.

Multicores
Typically, GPUs consists of many processor clusters that can be grouped together. This makes it possible to greatly boost a system’s processing power particularly to AI applications with parallel inputs of data, convolutional neural network (CNN), and training of ML algorithms.

Flexibility
Because of a GPU’s parallelism capabilities, you can group GPUs into clusters and distribute jobs among those clusters. Another option is to use individual GPUs with dedicated clusters for training specific algorithms. GPUs with high data throughput can perform the same operation on many data points in parallel, allowing them to process large amounts of data at unrivalled speed.

Dataset Size
For model training, AI algorithms require a large dataset, which accounts for memory-intensive computations. A GPU is one of the best options for efficiently processing datasets with many datapoints that are larger than 100GB in size. Since the inception of parallel processing, they have provided the raw computational power required for efficiently processing largely identical or unstructured data.

The two major hardware choices for running AI applications are FPGAs and GPUs. Although GPUs can handle the massive volumes of data necessary for AI and deep learning, they have limitations regarding energy efficiency, thermal issues, endurance, and the ability to update applications with new AI algorithms. FPGAs offer significant benefits for neural networks and ML applications. These include ease of AI algorithm updates, usability, durability, and energy efficiency.

Additionally, significant progress has been made in the creation of software for FPGAs that makes compiling and programming them simpler. For your AI application to be successful, you must investigate your hardware possibilities. As it is said, carefully weigh your options before settling on a course of action.

Softnautics AI/ML experts have extensive expertise in creating efficient Machine Learning solutions for a variety of edge platforms, including CPUs, GPUs, TPUs, and neural network compilers. We also offer secure embedded systems development and FPGA design services by combining the best design methodologies and the appropriate technology stacks. We help businesses in building high-performance cloud and edge-based AI/ML solutions like key-phrase/voice command detection, face/gesture recognition, object/lane detection, human counting, and more across various platforms.

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

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Emerging Trends and Challenges in Embedded System Design

Emerging Trends and Challenges in Embedded System Design

Platform Engineering /

An embedded system is a microprocessor based hardware system integrated with software, designed to handle a particular function or entire system functionalities. With the rapid growth in terms of technology and development in microcontrollers, embedded systems have also evolved in various forms. Embedded software is typically developed for handling specialized hardware in operating systems such as RTOS, Linux, Windows, and others. Furthermore, with the drastic increase in the adoption of embedded systems in the areas of machine learning, smart wearables, home automation, electronic design automation, and the advancement of multicore processing, the future of the embedded system market looks quite appealing. Between 2022 and 2031, the global market for embedded systems is anticipated to expand at a 6.5 percent CAGR and reach about $163.2 billion, as per Allied market research group reports.

An Overview of Embedded System design

In general, an embedded system consists of hardware, software, and embedded OS. The hardware comprises a user interface, memory, power supply, and communication ports. In the software section machine level code is being created with the use of programming languages like C and C++. RTOS (Real Time Operating System) is the most sorted out OS which is often used for the embedded operating system. Embedded system generally falls into three categories starting with small scale, medium scale, and sophisticated ones.

If you approach embedded system design without a plan, it can be overwhelming. A systematic approach, on the other hand, helps to divide the design cycle into manageable stages, allowing for proper planning, implementation, and collaboration.

The embedded system design consists of the following steps

Embedded system design process

Product identification/Abstraction
It all starts with requirement analysis, which starts with analysing product requirements and turning them into specifications. The number of inputs/outputs and the logic diagram are not the only considerations but investigating usage and operating conditions aid in determining the appropriate specifications for the embedded system.

Layout design
The hardware designer can begin building the blueprint once the requirements have been translated into specifications. At this stage, the design team must select the appropriate microcontrollers based on power consumption, peripherals, memories, and other circuit components keeping in mind the cost factor.

Printed circuit board
A PCB is an assembly that employs copper conductors to link various components electrically and to support them mechanically. A printed circuit board design involves a brainstorming process in which best practices for features and capabilities, and reliability must be followed. When working with high-speed mixed-signal circuits, microprocessors, and microcontrollers it becomes more complicated. The common types of PCBs include single & double sided, multi-layer, flex, ceramic, etc.

Prototype development
When creating a new product for a specific market segment, time is very essential and plays a crucial part. Creating a prototype allows you to identify flaws and design advantages early on. It aids in identifying design flaws earlier, allows ideas to be tested, determines product feasibility, and streamlines the design process.

Firmware development
Writing code for embedded hardware (microprocessor, microcontroller, FPGA), as opposed to a full-fledged computer, is known as firmware development. Software that controls the sensors, peripherals, and other components is known as firmware. To make everything function, firmware designers must use coding to make the hardware come to life. Utilizing pre-existing driver libraries and example codes provided by the manufacturer will speed up the process.

Testing & validation
Stringent testing must be passed before an embedded system design is authorized for production or deployment. The circuit must undergo reliability testing in addition to functionality testing, especially when operating close to its limitations.

Trends in embedded system
Technology trends are accelerating, and devices have developed into distinctive qualities that fit in many categories and sectors, including embedded. Due to its outcomes being application-oriented and advance development areas in focus, embedded systems and devices will gain more popularity in the coming future while considering various business sectors and their applications. Let us see recent trends under embedded systems.

System-on-Chip Solution
System on Chip (SoC) solution is another new trend in embedded system technology. Many businesses provide SoC based embedded devices, and among these solutions is the market delivery of analog and mixed-signal integrated circuits as a popular one. ASIC with great performance, small size, low cost, and IP protection is one such solution. Due to their size, weight, and power performance, it is very popular for application specific system needs.

Wireless technology
The primary goal of building wireless embedded software solutions is information transmission and reception. The wireless embedded system plays an important role where physical connections are impossible in any setting, and the use of IoT peripherals and devices becomes vital. With the technological advances in the areas of wireless solutions like Z-Wave, Bluetooth, Wi-Fi, and ZigBee the applicability of embedded wireless systems has drastically increased.

Automation
Every system in use today is becoming more automated. Every sector of growth has some level of automation, largely due to developments in computers, robots, and advancement in intelligent technologies like artificial intelligence and machine learning. The usage of embedded devices speeds up the connection of multiple storage components and can easily link up with cloud technology to power the device’s quick expansion of cognitive processing. The applications based on facial recognition and vision solution offers benefits like image identification & capturing, image processing, post processing, etc, and alerting for security in real time. For example, a smart factory outfitted with IoT, and artificial intelligence can significantly boost productivity by monitoring operations in real time and allowing AI to make decisions that prevent operational errors.

Low power consumption
The optimization of battery-powered devices for minimal power consumption and high uptime presents a significant challenge for developers. For monitoring and lowering the energy usage of embedded devices, a number of technologies/modules and design techniques are currently being developed and these include Wi-Fi modules, enhanced Bluetooth that use less power at the hardware layer optimizing embedded systems.

Challenges in embedded systems design
Embedded system design is an important component and is rapidly evolving; however, certain challenges must be addressed, such as issues related to security & safety, updating system hardware and software, consumption of power, seamless integration, and verification & testing which plays a crucial part in improving the performance of the system. When developing an embedded system, it is critical to avoid unexpected behaviour that could endanger users. It should be designed so that there are no problems with life-saving functionality in critical environments. Most of the time embedded device is controlled using mobile applications, where it is critical to ensure that there is no risk of data takeover or breach.

Writing code for embedded hardware (microprocessor, microcontroller, FPGA), as opposed to a full-fledged computer, is known as firmware development. Software that controls the sensors, peripherals, and other components is known as firmware. To make everything function, firmware designers must use coding to make the hardware come to life. Utilizing pre-existing driver libraries and example codes provided by the manufacturer will speed up the process

Embedded technologies will continue to grow, manufacturers are now heavily relaying the usage of embedded devices starting from automobiles to security systems, consumer electronics to smart home solutions, and others. Admittedly, the embedded system may now be the most important factor driving device cognition and performance advancements.

Softnautics offers the best design practices and the right selection of technology stacks to provide secured embedded systems, software development, and FPGA design services. We help businesses in building next-gen systems/solutions/products with services like platform enablement, firmware & driver development, OS porting & bootloader optimization, and Middleware Integration, and more across various platforms.

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

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Artificial Intelligence and Machine Learning based Image Processing

AI Engineering /

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.

Image processing can be employed to enhance an image’s quality, remove unwanted artefacts from an image, or even create new images completely from scratch. Nowadays, image processing is one of the fastest-growing technologies, and it has a huge potential for future wide adoption in areas such as video and 3D graphics, statistical image processing, recognising, and tracking people and things, diagnosing medical conditions, PCB inspection, robotic guidance and control, and automatic driving in all modes of transportation.

At Softnautics, we help industries to design Vision based AI solutions such as image classification & tagging, visual content analysis, object tracking, identification, anomaly detection, face detection and pattern recognition. Our team of experts have experience in developing vision solutions based on Optical Character Recognition, NLP, Text Analytics, Cognitive Computing, etc. involving various FPGA platforms.

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

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Voice assistant solutions

Next-Generation Voice Assisted Solutions

AI Engineering /

In recent years, voice technology has steadily increased in popularity, from voice control in vehicles to smart speakers in homes. A voice assistant solution is built using machine learning, NLP (Natural Language Processing), and voice recognition technology. These solutions merge cloud computing to combine AI and can converse with the end users in natural language. As modern buyers continue to want simplicity and user-friendly voice-enabled interactions, businesses now consider designing and implementing a conversational-first approach to forge closer bonds with them. According to Market Research Future, the global voice assistant market is predicted to reach USD 30.74 Billion by the end of 2030, with a CAGR of 31.2 % from 2020 to 2030. Voice assistants have a bright future and will get better at understanding the context of instructions, phrases, meanings and will be able to help us in a unique and more customized manner.

Voice Assisted Solution Model

A voice assistant model uses voice recognition and synthesis to listen to specific voice commands and perform specific functions as requested by the user. A voice assistant system comprises different stages starting with automatic speech recognition which entitles the device to identify and translate voice from speech to text. Afterward, text interpretation is performed using NLP (Natural Language Processing) which analyses speech in text form and understands the user’s intent, once the intent has been identified the desired action is carried through API (Application Programming Interface) connection and returned to the user as feedback using Text-to-Speech (TTS) technology.

Voice Assistant Solution Model

With the emergence of advanced AI technologies, companies may create synthetic speech that sounds like a human voice to resolve customer queries more effectively. Businesses across a variety of industries, including retail, automotive, media & entertainment, and healthcare, are realising the benefits of the technology and are using it to provide better, and more individualized customer experiences.

Applications of Voice Assisted Solutions

Automotive
Automakers are ensuring that their vehicles have the latest speech AI technology to suit customer demands and expectations, as more consumers desire in-car voice assistants. Voice assistant system can be easily integrated into the vehicle’s HMI (Human Machine Interface) through which different vehicle functions like music playback, windows adjustments, temperature control, and smartphone connectivity can be operated quite conveniently. With the use of voice assistant setting up the destination in the navigation system can be performed by voice commands, it also assists in calling other people without the use of any physical interference, as well as operating entertainment services that reduce the chances of driver distraction which lowers the number of accident cases. In addition, the voice assistant can communicate information to the victim’s family and the closest medical facility in the event of an accident. As a result, the level of safety is increased. With the advancement of AI in the fields of text-to-speech, NLP automakers can use different modes of voice as instructions depending on the driver’s situation. Auto manufacturers who want to stay ahead of the competition should seriously consider investing in voice AI technology.

Media & Entertainment
The media & entertainment sector is utilising voice assistants to offer people a tailored experience, rapid access to their favourite media, and quick, relevant search results. Voice assistants facilitate a rich and immersive experience with features like media asset management and interactive media where with voice instructions one can access all the media content. Users of voice assistants can control music, adjust the volume up and down, and skip tracks. In the case of smart TVs equipped with cutting-edge speech AI technology, they can comprehend difficult and compound inquiries as well as remember questions that have already been asked, making them more interesting and conversational. Voice assistants are also being used by entertainment apps to provide hands-free, rapid, and convenient user experiences for either the entire app or a specific feature.

Home Automation
It is now possible to use voice-controlled systems and devices to automate routine tasks because of technological breakthroughs. Having voice-controlled home automation gives you the ability to group all your home’s smart gadgets. Along with providing a remote-control option for entertainment devices like radios, music and songs, audiobooks, podcasts, etc., it assists with managing tasks like turning on and off the lights, fans, AC, door locks, curtains, etc. Home automation systems like Alexa and Google Home boost overall efficiency and can connect with several devices hassle-free. In addition, the integration of voice commands into the installation of such home automation with voice control offers greater security alternatives by streamlining the available security options. It helps reduce human efforts, especially for the elderly and disabled people.

Consumer Industry
As more voice-activated devices enter the market and more electronics makers start to integrate voice capability into current products and services, the popularity of voice assistants in the consumer industry is only anticipated to increase. Consumers will increasingly prefer using this technology to engage with their devices such as refrigerators, smart TVs, air conditioners, and all kinds of gadgets it comes up with in this era. Voice integrations make entertainment and social interaction more accessible to those with physical disabilities. Through information lookups, reminders, and routines to make calls, read/send emails, etc., they can help persons with memory impairments. Some of the areas under consumer electronics that are rapidly becoming popular with the inception of voice technology are smart wearable & fitness tracking, smart security & surveillance system, and digital personal assistants.

Voice assistants are becoming increasingly popular due to their capabilities that cut down on handling time and costs while maintaining accuracy and precision. They are getting better at decoding questions to provide timely, relevant answers. There are numerous opportunities for far richer and more in-depth interactions with clients. Voice assistant is becoming a technology that can’t be missed out, especially with the eventual rollout of 5G and the advancement in machine learning.

At Softnautics, we provide AI engineering and machine learning services and solutions with expertise on edge platforms (TPU, Rpi, FPGA), NN compilers for the edge, cloud platforms accelerators like AWS, Azure, AMD, and many more targeted for domains like Automotive, Multimedia, Industrial IoT, Consumer, and Security-Surveillance. Softnautics helps businesses in building high-performance cloud and edge-based ML solutions like key-phrase/voice command detection, VUI (Voice User Interface) design, hand gesture recognition, object/lane detection, and more across various platforms.

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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Embedded ML

An overview of Embedded Machine Learning techniques and their associated benefits

AI Engineering, Platform Engineering /

Owing to revolutionary developments in computer architecture and ground-breaking advances in AI & machine learning applications, embedded systems technology is going through a transformational period. By design, machine learning models use a lot of resources and demand a powerful computer infrastructure. They are therefore typically run-on devices with more resources, like PCs or cloud servers, where data processing is efficient. Machine learning applications, ML frameworks, and processor computing capacity may now be deployed directly on embedded devices, thanks to recent developments in machine learning, and advanced algorithms. This is referred to as Embedded Machine Learning (E-ML).

The processing is moved closer to the edge, where the sensors collect data, using embedded machine learning techniques. This aids in removing obstacles like bandwidth and connection problems, security breaches by data transfer via the internet, and data transmission power usage. Additionally, it supports the use of neural networks and other machine learning frameworks, as well as signal processing services, model construction, gesture recognition, etc. Between 2021 to 2026, the global market for embedded AI is anticipated to expand at a 5.4 percent CAGR and reach about USD 38.87 billion, as per the maximize market research group reports.

The Underlying Concept of Embedded Machine Learning

Today, embedded computing systems are quickly spreading into every sphere of the human venture, finding practical use in things starting from wearable health monitoring systems, wireless surveillance systems, networked systems found on the internet of things (IoT), smart appliances for home automation to antilock braking systems in automobiles. The Common ML techniques used for embedded platforms include SVMs (Support Vector Machine), CNNs (convolutional neural network), DNNs (Deep Neural networks), k-NNs (K-Nearest Neighbour), and Naive Bayes. Large processing and memory resources are needed for efficient training and inference using these techniques. Even with deep cache memory structures, multicore improvements, etc., general-purpose CPUs are unable to handle the high computational demands of deep learning models. The constraints can be overcome by utilizing resources such as GPU and TPU processors. This is mainly because sophisticated linear algebraic computations, such as matrix and vector operations, are a component of non-trivial deep learning applications. Deep learning algorithms can be run very effectively and quickly on GPUs and TPUs, which makes them ideal computing platforms.

Running machine learning models on embedded hardware is referred to as embedded machine learning. The latter works according to the following fundamental precept: While model execution and inference processes take place on embedded devices, the training of ML models like neural networks takes place on computing clusters or in the cloud. Contrary to popular belief, it turns out that deep learning matrix operations can be effectively carried out on hardware with constrained CPU capabilities or even on tiny 16-bit/32-bit microcontrollers.

The type of embedded machine learning that uses extremely small pieces of hardware, such as ultra-low-power microcontrollers, to run ML models is called TinyML.Machine Learning approaches can be divided into three main categories: reinforcement learning, unsupervised learning, and supervised learning. In supervised learning, labelled data can be learned; in unsupervised learning, hidden patterns in unlabelled data can be found; and in reinforcement learning, a system can learn from its immediate environment by a trial-and-error approach. The learning process is known as the model’s “training phase,” and it is frequently carried out utilizing computer architectures with plenty of processing power, like several GPUs. The trained model is then applied to new data to make intelligent decisions after learning. The inference phase of the implementation is what is referred to as this procedure. IoT and mobile computing devices, as well as other user devices with limited processing resources, are frequently meant to do the inference.

 

Machine Learning Techniques

Application Areas of Embedded Machine Learning

Intelligent Sensor Systems
The effective application of machine learning techniques within embedded sensor network systems is generating considerable interest. Numerous machine learning algorithms, including GMMs (Gaussian mixture model), SVMs, and DNNs, are finding practical uses in important fields such as mobile ad hoc networks, intelligent wearable systems, and intelligent sensor networks.

Heterogeneous Computing Systems
Computer systems containing multiple types of processing cores are referred to as heterogeneous computing systems. Most heterogeneous computing systems are employed as acceleration units to shift computationally demanding tasks away from the CPU and speed up the system. Heterogeneous Multicore Architecture is an area of application where to speed up computationally expensive machine learning techniques, the middleware platform integrates a GPU accelerator into an already-existing CPU-based architecture thereby enhancing the processing efficiency of ML data model sets.

Embedded FPGAs
Due to their low cost, great performance, energy economy, and flexibility, FPGAs are becoming increasingly popular in the computing industry. They are frequently used to pre-implement ASIC architectures and design acceleration units. CNN Optimization using FPGAs and OpenCL-based FPGA Hardware Acceleration are the areas of application where FPGA architectures are used to speed up the execution of machine learning models.

Benefits

Efficient Network Bandwidth and Power Consumption
Machine learning models running on embedded hardware make it possible to extract features and insights directly from the data source. As a result, there is no longer any need to transport relevant data to edge or cloud servers, saving bandwidth and system resources. Microcontrollers are among the many power-efficient embedded systems that may function for long durations without being charged. In contrast to machine learning application that is carried out on mobile computing systems which consumes a substantial amount of power, TinyML can increase the power autonomy of machine learning applications to a greater extent for embedded platforms.

Comprehensive Privacy
Embedded machine learning eliminates the need for data transfer and storage of data on cloud servers. This lessens the likelihood of data breaches and privacy leaks, which is crucial for applications that handle sensitive data such as personal information about individuals, medical data, information about intellectual property (IP), and classified information.

Low Latency
Embedded ML supports low-latency operations as it eliminates the requirement of extensive data transfers to the cloud. As a result, when it comes to enabling real-time use cases like field actuating and controlling in various industrial scenarios, embedded machine learning is a great option.

Embedded machine learning applications are built using methods and tools that make it possible to create and deploy machine learning models on nodes with limited resources. They offer a plethora of innovative opportunities for businesses looking to maximize the value of their data. It also aids in the optimization of the bandwidth, space, and latencies of their machine learning applications.

Softnautics AI/ML experts have extensive expertise in creating efficient ML solutions for a variety of edge platforms, including CPUs, GPUs, TPUs, and neural network compilers. We also offer secure embedded systems development and FPGA design services by combining the best design methodologies with the appropriate technology stacks. We help businesses in building high-performance cloud and edge-based ML solutions like object/lane detection, face/gesture recognition, human counting, key-phrase/voice command detection, and more across various platforms.

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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FPGA Market Trends With Next-Gen Technology

Platform Engineering, VLSI Design & Verification /

Due to their excellent performance and versatility, FPGAs (Field Programmable Gate Arrays) appeal to a wide spectrum of businesses. Also, it has the feature of adopting new standards and modifying hardware as per the specific application requirement even after it’s been deployed for usage. ‘Gate arrays,’ on the other hand, relate to the architecture’s two-dimensional array of logic gates. FPGAs are used in several applications where complicated logic circuitry is required and changes are expected. Medical Devices, ASIC Prototyping, Multimedia, Automotive, Consumer Electronics, and many other areas are covered by FPGA applications. In recent years, market share and technological innovation in the FPGA sector is growing at a rapid speed. FPGAs offer benefits for Deep Learning and Artificial Intelligence based solutions, including an improved performance with low latency and high throughput, and power efficiency. According to Mordor Intelligence, the global FPGA market was valued at USD 6958.1 million in 2021, and it is predicted to reach USD 11751.8 million by 2027, with a CAGR of 8.32 percent from 2022 to 2027.

FPGA Design Market Drivers

Global Market Drivers

Let’s look at some interesting real-world applications that can be built using TensorFlow Lite on edge TPU.

 

The FPGA market is highly contested due to economies of scale, the nature of product offerings, and the cost-volume metrics favouring firms with low fixed costs. According to the size, 28nm FPGA chips are expected to grow rapidly because they provide high-speed processing and enhanced efficiency. These features have aided its adoption in a variety of industries, including automobiles, high-performance computing, and communications. The consumer electronics sector appears to be promising for FPGA since rising spending power in developing countries contributes to increased market demand for new devices. FPGAs are being developed by market players for use in IoT devices, Natural Language Processing (NLP), based infotainment, multimedia systems, and various industrial smart solutions. Based on the application requirement, either low-end, mid-range or high-end FPGA configurations are selected.

FPGA Architecture Overview

The general FPGA architecture design consists of three types of modules. They are I/O blocks, Switch Matrix, and Configurable Logic Blocks (CLB). FPGA is a semiconductor device made up of logic blocks coupled via programmable connections.

FPGA Architecture

 

The logic blocks are made up of look-up tables (LUTs) with a set number of inputs and are built using basic memory such as SRAM or Flash to hold Boolean functions. To support sequential circuits, each LUT is connected to a multiplexer and a flip-flop register. Similarly, many LUTs can be used to build for handling complex functions. As per the configurations FPGAs are classified into three types low-end, Mid-end & High-end FPGAs. Artix-7/Kintex-7 series from Xilinx, ECP3, and ECP5 series from Lattice semiconductor are some of the popular FPGA designs for low power & low design density. Whereas Virtex family from Xilinx, ProASIC3 family from Microsemi, Stratix family from Intel are designed for high performance with high design density.

FPGA Firmware Development

Since the FPGA is a programmable logic array, the logic must be configured to match the system’s needs. Firmware, which is a collection of data, provides the configuration. Because of the intricacy of FPGAs, the application-specific purpose of an FPGA is designed using the software. The user initiates the FPGA design process by supplying a Hardware Description Language (HDL) definition or a schematic design. VHDL (VHSIC Hardware Description Language) and Verilog are two commonly used HDLs. After that, the next step in the FPGA design process is to develop a netlist for the FPGA family being used. This is developed using an electronic design automation program and outlines the connectivity necessary within the FPGA. Afterward, the design is committed to the FPGA, which allows it to be used in the (ECB) electronic circuit board for which it was created.

Applications of FPGA

Automobiles
FPGAs in automobiles are extensively used in LiDAR to construct images from the laser beam. They’re employed in self-driving cars to instantly evaluate footage for impediments or the road’s edge for obstacle detection. Also, FPGAs are widely used in car-infotainment systems for reliable high-speed communications within the car. They enhance efficiency and conserve energy.

Tele-Communication Systems
FPGAs are widely employed in communication systems to enhance connectivity and coverage and improve overall service quality while lowering delays and latency, particularly when data alteration is involved. Nowadays FPGA is widely used in server and cloud applications by businesses.

Computer Vision Systems
These systems are becoming increasingly common in today’s world. Surveillance cameras, AI-bots, screen/character readers, and other devices are examples of this. Many of these devices necessitate a system that can detect their location, recognize things in their environment, and people’s faces, and act and communicate with them appropriately. This functionality necessitates dealing with large volumes of visual data, constructing multiple datasets, and processing them in real-time, this is where FPGA accelerates and makes the process much faster.

The FPGA market will continue to evolve as the demand for real-time adaptable silicon grows with next-gen technologies Machine Learning, Artificial Intelligence, Computer Vision, etc. The importance of FPGA is expanding due to its adaptive/programming capabilities, which make it an ideal semiconductor for training massive amounts of data on the fly. It is promising for speeding up AI workloads and inferencing. The flexibility, bespoke parallelism, and ability to be reprogrammed for numerous applications are the key benefits of using an FPGA to accelerate machine learning and deep learning processes.

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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Machine Learning Based Facial Recognition and Its Benefits

Machine Learning Based Facial Recognition and Its Benefits

AI Engineering /

Machine Learning based facial recognition is a method of utilizing the face to identify or confirm one’s identity. Persons can be identified in pictures, films, or real time using facial recognition technology. Facial recognition has traditionally functioned in the same way as other biometric methods including voice recognition, eye irises, and fingerprint identification.

The growing use of facial recognition technology in a variety of applications is propelling the industry forward. In case of security, authorities are employing this technology to verify a passenger’s identity, particularly at airports. Face recognition software is also being used by law enforcement agencies to scan faces taken on CCTV and locate the suspect. Smartphones are another area of application where the technology has seen widespread adoption where the software is used to unlock the phone and verify payment information. As in the case of automotives self-driving cars are the focus of using this technology to unlock the car and act as the key to start/ stop the car. According to a report published by markets & markets group, the global facial recognition market is expected to grow at a CAGR of 17.2 percent over the forecast period, from USD 3.8 billion in 2020 to USD 8.5 billion in 2025.

Facial Recognition Technology Working Mechanism

A computer examines visual data and searches for a specified set of indicators, such as a person’s head shape, depth of their eyelids, etc. A database of facial markers is built, and an image of a face that matches the database’s essential threshold of resemblance suggests a possible match. Face recognition technologies, such as machine vision, modelling and reconstruction, and analytics, require the utilization of advanced algorithms in the areas of Machine Learning – Deep Learning and CNN (Convolutional Neural Network), which is growing at an exponential rate.

As facial recognition technology has progressed, a variety of systems for mapping faces and storing facial data have evolved based on Computer Vision, Deep Learning each with various degrees of accuracy and efficiency. In general, there exist 3 methods, which are as follows.

  • Traditional facial recognition
  • Biometric facial recognition
  • 3D facial recognition
Traditional Facial Recognition

There are two methods to it. One is holistic facial recognition, in which an identifier’s complete face is analysed for identifying traits that match the target. Feature-based facial recognition, on the other hand, separates the relevant recognition data from the face before applying it to a template that is compared against prospective matches.

Detection – Facial recognition software detects the identifier’s face in an image
Analysis – Algorithms determine the unique facial biometrics and features, such as the distance between nose and mouth, size of eyelids, forehead, and other characteristics
Identification – The software can now compare the target faceprint to other faceprints in the database to find a match

Overview of Facial Recognition System

Biometric Facial Recognition

Skin and face biometrics are a growing topic in the field of facial recognition that has the potential to improve the accuracy of facial recognition technologies dramatically. A skin texture analysis examines a specific area of a subjects’ skin, using an algorithm to take very precise measurements of wrinkles, textures, and pores.

3D Facial Recognition

It’s a technique that uses the three-dimensional geometry of the human face to create a three-dimensional model of the facial surface. It employs specific aspects of the face to identify the subject, such as the curvature of the eye socket, nose, and chin, where hard tissue and bone are most visible. These regions are all distinct from one another and do not change throughout time. 3D face recognition can achieve more accuracy than its 2D counterpart by analysing the geometry of hard properties on the face. In the 3D facial recognition technology, sensors are employed to capture the shape of the face with more precision. Unlike standard facial recognition systems, 3D facial recognition is unaffected by light, and scans can even be done in complete darkness. Another advantage of 3D facial recognition is that it can recognize a target from many angles rather than just a straight-on appearance.

Applications of Facial Recognition Technology Retail

Face recognition in retail opens an ample number of possibilities for elevating the customer experience. Store owners can collect data about their customers’ visits (such as their reactions to specific products and services) and then conclude how to personalize their offerings. They can offer unique product packages to the clients based on their previous purchasing history and insights. Vending machines in Japan, for example, proposes drinks to customers based on their gender and age using facial recognition technology.

Healthcare

It has enhanced patient experience and reduced efforts for healthcare professionals by improving security and patient identification, as well as better patient monitoring and diagnosis. When a patient walks into the clinic, the facial recognition system scans their face and compares it to a database held by the hospital. Without the need for paperwork or other identification documents, the patient’s identity and health history are verified in real-time.

Security Companies

Nowadays machines that can effectively recognize individuals open a host of options for the security industry, the most important of which is the potential to detect illicit access to areas where non-authorized people are prohibited. Artificial intelligence-powered face recognition software can help spot suspicious behaviour, track down known offenders, and keep people safe in crowded locations.

Fleet Management Services

Facial recognition could be used in fleet management to give alerts to unauthorized personnel attempting to obtain access to vehicles, preventing theft. The fact that distraction is the major cause of accidents, which is due to the usage of electronic gadgets. When a driver’s eyes aren’t on the road, facial recognition technology may be designed to detect it. It may also be trained to detect eyes that indicate an intoxicated or tired driver, improving the safety of driver & fleet vehicles.

Benefits of Facial Recognition Technology

With constantly evolving capabilities, it will be fascinating to see where Machine Learning based Facial Recognition technology will reach over next decade. The amount and quality of image data required to train any facial recognition program are critical to its performance. Many examples are required, and each one necessitates a significant number of pictures to develop a thorough comprehension of the face.

At Softnautics we offer Machine Learning services to assist organizations in the development of futuristic AI solutions like facial recognition systems, Machine Learning/Deep Learning algorithms that compare facial features to several data sets using random and view-based features, utilizing complex mathematical representations and matching methods. We develop powerful Machine Learning models for feature analysis, neural networks, eigenfaces, and automatic face recognition. We provide Machine Learning services and solutions with expertise on edge platforms (TPU, RPi), NN compiler for the edge, Computer Vision, Machine Vision, tools like TensorFlow, TensorFlow Lite, Docker, GIT, AWS deepLens, Jetpack SDK, and many more targeted for domains like Automotive, Multimedia, Industrial IoT, Healthcare, Consumer, and Security-Surveillance.

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

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An Overview of Automotive Functional Safety Standards and Compliances

Quality Engineering /

It has been observed that the frequency of traffic accidents has increased significantly over the last two decades, resulting in many fatalities. As per the WHO (World Health Organization) road safety report across the globe, about 1.2 million people lose their life on the roads each year, with another 20 to 50 million suffering quasi-injuries. One of the primary elements that have a direct impact on road user safety is the reliability of automobile devices and systems.

Autonomous vehicles are gaining immense popularity with the advancement in self-driving. Wireless connectivity and other substantial technologies are facilitating ADAS (Advanced Driver Assistant Systems), which consists of applications like adaptive cruise control, automated parking, navigation system, night vision & automatic emergency braking, etc, which play a critical role in the development of fully autonomous vehicles.

Safety Of The Intended Functionality SOTIF (ISO/PAS 21448) was created to solve the new safety challenges that software developers are encountering for autonomous (and semi-autonomous) vehicles. SOTIF (ISO 21448) refers to safety-critical functionality that necessitates sufficient situational awareness. By implementing these procedures, you can accomplish safety in situations where you might otherwise fail. SOTIF (ISO 21448) was designed to be ISO 26262: Part 14 at first. Since assuring safety in the absence of a system breakdown is so difficult, SOTIF (ISO 21448) has become its standard. Because AI and Machine Learning are the vital components of autonomous vehicles. The use of SOTIF (ISO 21448) will be critical in guaranteeing that AI can make appropriate judgments and avoid dangers.

Functional Safety – ISO 26262

FuSa (ISO 26262) automotive functional safety standard establishes a safety life cycle for automotive electronics, requiring designs to pass through an overall safety process to comply with the standard. As within the case of IEC (International Electrotechnical Commission), 61508 measures the reliability of safety functions and uses maximum probability while ISO 26262 is predicated on the violation of safety goals and provides requirements to realize a suitable level of risk. ISO 26262 validates a product’s compliance from conception to decommissioning to develop safety-compliant systems.

ISO 26262 employs the idea of Automotive Safety Integrity Levels (ASILs), a refinement of Safety Integrity Levels, to reach the objective of formulating and executing reliable automotive systems and solutions. ASILs are assigned to components and subsystems that have the potential to cause system failure and malfunction, resulting in hazards. The best allocation of safety levels to the system framework is a complicated issue that must ensure that the highest safety criteria are met while the development cost of the automobile system is kept to a minimum. Let us see what each part of this standard reflects.

Automotive Functional Safety Guidelines

Part 1 – Vocabulary: It relates to the definitions, terms, and abbreviations used in the standard to maintain unity and avoid misunderstanding.

Part 2 – Management of Functional Safety: It offers information on general safety management as well as project-specific information on management activities at various stages of the safety lifecycle.

Part 3 – Concept Phase: Analysis and assessment of risk are being evaluated in the early product development phase.

Part 4 – Product Development at the System Level: It covers system-level development issues comprising system architecture design, item integration & testing.

Part 5 – Product Development at the Hardware Level: It covers basic hardware level design and evaluation of hardware metrics.

Part 6 – Product Development at the Software Level: It comprises software safety, design, integration & testing of embedded software.

Part 7 – Production and Operation: This section explains how to create and maintain a production process for safety-related parts and products that will be installed in vehicles.

Part 8 – Support Processes: This section covers all stages of a product’s safety lifecycle, like proceeding to verification, undertaking tool qualification, documentation etc.

Part 9 – Automotive Safety Integrity Level (ASIL): It covers the requirement for ASIL analysis, defines ASIL decomposition state and analysis of dependent failures.

Part 10 – Guideline on ISO 26262: It covers an overview of ISO 26262 and other guidelines on how to apply the standard.

ISO 26262 classifies ASILs into four categories: A, B, C, and D. The lowest degree of automobile hazard is ASIL A, while the maximum degree is ASIL D. Since the dangers connected with their failure is the highest, systems like airbags, anti-lock brakes, and power steering require an ASIL-D rating, the highest level of rigor applied to safety assurance. Components like rear lights, on the other hand, are merely required to have an ASIL-A rating. ASIL-B would be used for headlights and brake lights, while ASIL-C would be used for cruise control.

Types-of-ASIL-classification

Types of ASIL classification

Automotive Safety Integrity Levels are determined by two factors such as analysis of hazard and assessment of risk. Engineers measure three distinct factors for each electronic component in a vehicle, and those are based on the following factors.

  • Intensity (the severity of the driver’s and passengers’ injuries)
  • Amount of exposure (how frequently the vehicle is subjected to the hazard)
  • Possibility of control (how much the driver can do to avoid an accident.)
MISRA C

The Motor Industry Software Reliability Association (MISRA) publishes standards for the development of safety and security-related electronic systems, embedded control systems, software-intensive applications, and independent software.

MISRA C contains components that protect automobile software from errors and failures. With over 140 rules for MISRA–C and more than 220 rules for MISRA–C++, the suggestions tackle code safety, portability, and reliability issues that affect embedded systems. For MISRA C compliance, developers must follow a set of mandatory rules. The goal of MISRA C is to provide the best performance in terms of software operation for software programs used in automobiles, as these programs can have a significant impact on the vehicle’s overall design safety. Developers utilize MISRA C as one of the tools for developing safe software for automobiles.

AUTOSAR

AUTOSAR (Automotive Open System Architecture) this standard’s goal is to provide a set of specifications that describe fundamental software modules, specify programmatic links, and implement common methods for further development using a standardized format.

AUTOSAR’s sole purpose is to provide a uniform standard across manufacturers, software suppliers, and tool developers while maintaining competition so that the result of the business is not harmed.

While reusability of software components lowers development costs and guarantees stability, it also increases the danger of spreading the same software flaw or vulnerability to other products that use the same code. To solve this significant issue, AUTOSAR advocates safety and security features in software architecture.

The design approach of AUTOSAR includes

  • Product and system definition including software, hardware, and complete system.
  • Allocating AUTOSAR to each ECU (Electronic Control Unit)
  • Configuration of OS, drivers, and application for each ECU (Electronic Control Unit)
  • Comprehensive testing to validate each component, at unit level and system level.

The necessity to assure functional safety at every level of product development and commissioning has grown even more crucial in today’s world when automotive designs have got increasingly complicated with many ECUs, sensors, and actuators. Therefore, today’s automakers are more concerned about adhering to the highest automobile safety requirements, such as the ISO 26262 standard and ASIL Levels.

At Softnautics, we help automotive businesses to manufacture devices/chipsets complying with automotive safety standards and design Machine Learning based intelligent solutions such as automatic parallel parking, traffic sign recognition, object/lane detection, in-vehicle infotainment systems, etc. involving FPGAs, CPUs, and Microcontrollers. Our team of experts has experience working with autonomous driving platforms, middleware, and compliances like adaptive AUTOSAR, FuSa (ISO 26262), and MISRA C. We support our clients in the entire journey of intelligent automotive solution design.

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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