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.

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.

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.

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

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At its core, Edge AI is the combination of Edge computing and Edge intelligence to run machine learning tasks directly on end devices which generally consists of an in-built microprocessor and sensors, while the data processing task is also completed locally and stored at the edge node end. The implementation of machine learning models in edge AI will decrease the latency rate and will improve the network bandwidth. Edge AI helps applications that rely on real-time data processing by assisting with data, learning models, and inference. The edge AI hardware market valued at USD 6.88 billion is expected to reach USD 39 billion by 2030 at a CAGR of 18.8% as per the report by Valuates Reports.

The advancement of IoT & adoption of smart technologies by consumer electronics and automotive among others are fuelling the AI hardware market forward. Edge AI processors with on-device analytics are going to enhance the opportunities for the AI hardware market. NVIDIA, Google, AMD, Lattice, Xilinx, and Intel are some of the edge computing platforms providers for such cognitive AI applications design. Furthermore, the advancement of emerging technologies such as deep learning, AI hardware accelerators, neural networks, computer vision, optical character recognition, natural language processing, etc. opens all-new horizons of opportunities. While businesses are rapidly moving towards a decentralized computer architecture, they are also discovering new ways to use this technology to increase productivity.

What is Edge Computing?

Edge computing brings the computing and storage of data closer to the devices that collect it, rather than relying on a primary site that might be far away. This ensures that data does not suffer from latency and redundancy issues that limit an application’s efficiency. The amalgamation of Machine Learning into edge computing gives rise to new, resilient, and scalable AI systems in a wide range of industries.

Myth: Will Edge Computing suppress Cloud Computing?

No, edge computing is not going to replace nor suppress cloud computing, instead, the edge will complement with cloud environment for better performance and leverage machine learning tasks to a greater extent.

Need for Edge AI Hardware Accelerators

Running complex machine learning tasks on edge devices requires specialized AI hardware accelerators, which boost speed & performance, offer greater scalability, maximum security, reliability & efficient data management.

VPU (Vision Processing Unit)

A vision processing unit is a sort of microprocessor aimed at accelerating machine learning and AI algorithms. It balances Edge AI workload with high efficiency and supports tasks like image processing, which is like a video processing unit used with neural networks. It works on low power and high-performance precision.

GPU (Graphical Processing Unit)

An electronic circuit capable of producing graphics for display on an electronic device is referred to as a GPU. It can process multiple data simultaneously, making them ideal for machine learning, video editing, and gaming applications. With their ability to perform complex machine learning tasks, they are being extensively used in mobiles, tablets, workstations, and gaming consoles nowadays.

TPU (Tensor Processing Unit)

Google introduced the Tensor Processing Unit (TPU), an ASIC for executing Machine Learning (ML) algorithms based on neural networks. It uses less energy and operates more efficiently. Google Cloud Platform with TPUs is a good choice for ML applications that don’t require a lot of cloud infrastructure.

Applications of Edge AI across industries

Smart Factories

Edge AI can be applied for predictive maintenance belonging to the equipment industry, by which edge devices can perform analysis on stored data to identify scenarios wherein a failure might occur before the actual failure happens.

Autonomous Vehicles

Self-driving vehicles are one of the best examples of incorporating edge AI technology into the automobile industry, where the integration helps detection and identification of objects thereby eliminating chances for accidents. It aids in avoiding collision with pedestrians/other vehicles, detecting roadblocks, which requires immediate real-time data processing, as plenty of lives are at stake.

Edge AI

Industrial IoT

With the enablement of Computer Vision to Industrial IoT, visual inspections can be done effortlessly without much human intervention thereby increasing operational efficiency and improving the productivity in assembly lines.

Smart Healthcare

Edge Artificial intelligence can help in the healthcare industry via wearables enhancing surveillance of a patient’s health and forecasting early disorders. These details can also be used to provide patients with effective treatments in real-time. Patient data can be secured with HIPAA compliance in place.

Benefits of using Machine Learning on Edge

Higher Scalability
As the demand for the number of interconnected IoT devices is on the rise across industries, Edge AI is becoming an absolute choice due to its efficient and timely data processing without relying heavily on a cloud-based centralized network.

Data Protection & Security
Since Edge devices are not completely dependent on cloud resources, attackers cannot bring down the whole cloud data center/server system to a standstill point.

Low Operational Risks
Since Edge AI is based on a distributed model, in case of failure it will not affect the entire system chain, as in the case of cloud, which is based on a centralized model. The failure of individual edge devices will not pose a huge threat to the entire system.

Reduced Latency Rate
With the implementation of Edge AI, the computation can be performed in milliseconds. This is possible as there is no need to send data to the cloud for initial processing, thereby saving time and reduction of latency in the data processing.

Cost-effectiveness
Edge AI saves a lot of bandwidth, as the transfer of data is minimized. This also reduces the capacity requirements for cloud services which makes Edge AI a cost-effective solution, when compared to cloud-based ML solutions.

In several instances, machine learning models are complex and quite big. In such situations, it becomes extremely difficult to shift these models to compact edge devices. Without proper precautions if efforts are put to reduce the complexity of the algorithms, the processing perfection will take a toll, and also, the computation power will become limited. Hence at the initial development stage, it’s crucial to evaluate all the failure points. Most priority should be given to testing the trained model perfectly on different types of devices and operating systems.

At Softnautics, we provide machine learning services and solutions with expertise on edge platforms (TPU, RPi), NN compiler for the edge, and 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. Softnautics can help businesses to build high-performance edge ML solutions like object/lane detection, face/gesture recognition, human counting, key-phrase/voice command detection, and more across various platforms. Our team of experts has years of experience working on various edge platforms, cloud ML platforms, and ML tools/ technologies.

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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Overview

Xilinx’s new heterogeneous compute platform, Versal Adaptive Compute Acceleration Platform (ACAP), efficiently combines the power of software and hardware programmability. Versal ACAP devices are used for a wide range of applications such as data center, Wireless 5G, AI/ML, A & D Radars, Automotive, and wired applications.

Hardware Architecture

Versal ACAP is powered by scalable, adaptable, and Intelligent engines. On-chip memory access for all the machines is enabled via network on chip (NoC).

Source: Xilinx

Scalar Engines

Scalar engines power platform computing, decision making, and control. For general-purpose computing, dual-core ARM cortex- A72 Application Processing Unit (APU) is used in versal. APU supports virtualization allowing multiple software stacks to run simultaneously. The dual core ARM cortex R5F Realtime Processing Unit (RPU) is available for real-time applications. RPU can be configured as a single/dual processor in lockstep mode. RPU can be used for variety of time-critical applications, e.g., safety in the automotive domain.

Platform Management Controller

Platform Management Controller (PMC) is responsible for boot, configuration, partial re-configuration, and general platform management tasks, including power, clock, pin control, reset management, and system monitoring. It is also responsible for device life cycle management, including security.

Adaptable Engines

The adaptable engines feature the classic FPGA technology – the programable silicon. Adaptable engines include DSP engines (Adaptable), configurable logic blocks (Intelligent), and two types of RAM (Block RAM and Ultra RAM (adaptable)). Using such a configurable structure, users can create any kind of accelerator for different kinds of applications.

Intelligent Engines

AI engines are software programable and hardware adaptable. They are an array of VLIW SIMD vector processors used for ML/AI inference and advanced signal processing. AI engine is tile-based architecture. Each tile is made of a vector processor, scaler processor, dedicated program and data memory, dedicated AXI data movement channels, DMA, and locks.

Network on Chip

Network on Chip (NoC) makes Versal ACAPs even more powerful by connecting all engines, memory hierarchy, and highspeed IOs. NoC makes each hardware component and soft IP modules accessible to each other and the software via a memory-mapped interface.

Software Support

Xilinx introduced Vitis – A unified software development platform that enables embedded software and accelerated applications on heterogeneous Xilinx platforms, including FPGAs, SoCs, and Versal ACAPs.

The Vitis unified software development platform provides sets of open-source libraries, enabling developers to build hardware-accelerated applications without hardware knowledge. It also provides Xilinx Runtime Library (XRT), including firmware, board utilities, kernel driver, user-space libraries, and APIs. Vitis also provides an AI development environment including a deep learning framework like TensorFlow, PyTorch, and Caffe, and offers comprehensive APIs to prune, quantize, optimize, debug, and compile trained networks to achieve the highest AI inference performance.

Source: Xilinx

Softnautics have a wide range of expertise on various platforms including vision and image processing on VLIW SIMD vector processor, FPGA design development, Linux kernel driver development, Platform and Power Management Multimedia development.

Softnautics is developing high-performance Vision & ML/AI solutions using Versal ACAP by utilizing high bandwidth and configurable NoC, AI engine tile array in tandem with DMA and interconnect with PL. Versal’s high bandwidth interfaces and high compute processors can improve performance. One such use-case that Softnautics is already developing Scene Text Detection solution using Vitis AI & DPU.

The Scene Text Detection use-case demands high power compute for LSTM operations. Our AI/ML engineers’ team evaluates their design to leverage the custom memory hierarchy and the multicast stream capability on AI interconnect and AI-optimized vector instructions to gain the best performance. With a powerful AI Engine DMA capability and ping-pong buffering of stream data onto local tile memory, the ability of parallel processing opens a plethora of optimized implementations. Direct memory access (DMA) in the AI Engine tile moves data from the incoming stream(s) to local memory and from local memory to outgoing stream(s). Configuration interconnect (through memory-mapped AXI4 interface) with a shared, transaction-based switched interconnect provides access from external masters to internal AI Engine tile.

Further, cascade streams across multiple AI Engine tiles allow for greater flexibility in design by accommodating multiple ML inference instances. Along with the deep understanding of Versal ACAP memory hierarchies, AI Engine Tiles, DMA, and parallel processing, Softnautics’ extensive experience in leading ML/AI frameworks TensorFlow, PyTorch, and Caffe aids in creating end to end accelerated ML/AI pipelines with a focus on pre/post-processing of streams and model customization.

Softnautics has also been an early major contributor in Versal ACAP Platform Management related developments. Some of the key contributions in this space involve developing software components on Versal, such as platform management library (xilpm), Arm Trusted Firmware, Linux device drivers, u-boot for Platform Management.

Through our hands-on experience on Versal ACAP for AI/ML, Machine Vision & Platform Management, Softnautics can help customers take their concepts to design & deployment in a seamless fashion.

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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