Hardware Accelerators : A Comprehensive Guide

Our computers are similar to a bicycle that is trying to climb up the steep hill of data. It’s slow, it struggles, and it can’t handle heavy loads. It needs a very powerful engine to climb. Hardware accelerators serve as a turbo boost for our CPUs and computers to encourage massive processing and computation swiftly. 

What’s in this Guide?

In today’s rapidly evolving technological landscape, the convergence of artificial intelligence (AI) and hardware acceleration has emerged as a game-changer. From model deployment to real-time inference, AI-hardware accelerators are pivotal in enhancing the efficiency and performance of AI applications across diverse domains. This comprehensive guide delves deeper into the technical aspects, practical applications, and benefits of AI-hardware accelerators, focusing on deployment kits, development boards, edge devices, and GPUs.

 

What are Hardware Accelerators?

A hardware accelerator is a specialised component or subsystem within an edge device that is dedicated to accelerating specific computations, such as AI inferencing, image processing, video encoding and decoding, cryptography, and more. By leveraging hardware accelerators, edge devices can perform complex computations faster, reduce latency, conserve energy, and enhance overall performance. Here are some common types of hardware accelerators and their frameworks and applications.

 

 

Hardware Accelerator	Applications	Technical Details	Frameworks Support
GPU	Computer Graphics, General-Purpose Computing, AI Training/Inference, Ray Tracing, Video Processing.	Parallel processing architecture with numerous cores. High memory bandwidth and capacity. Optimized for floating-point operations.	CUDA, OpenCL, TensorFlow, PyTorch, Caffe
TPU	Machine Learning, Neural Networks, AI Acceleration.	Customized tensor processing units for matrix operations. Optimized for deep learning workloads.  High throughput and low latency execution.	TensorFlow, TensorFlow Lite, TensorFlow Extended
NPU	AI Inference, Edge Computing.	Programmable logic cells for flexible hardware configurations. Low-level control over hardware resourceS. Support for custom algorithms and protocols	TensorFlow, PyTorch, ONNX
FPGA	Custom Hardware Acceleration, Reconfigurable Computing.	Programmable logic cells for flexible hardware configurations<br>- Low-level control over hardware resources<br>- Support for custom algorithms and protocols	Intel OpenCL SDK, Xilinx Vivado, Apache OpenCL
SoC	Embedded Systems, IoT Devices	Integration of CPU, GPU, and other components on a single chip. Low power consumption for mobile and IoT applications. Support for hardware acceleration and peripherals.	TensorFlow Lite, PyTorch Mobile, Edge TPU
DSP	Digital Signal Processing, Audio Processing	Specialized hardware for signal processing tasks. High-speed processing of audio and sensor data. Efficient power management and real-time capabilities	MATLAB, Simulink, C/C++
ASIC’s	Cryptography, AI Acceleration	Custom-designed circuits for specific applications. High-performance and low-power consumption. ASIC libraries for encryption and AI operations	TensorFlow, PyTorch, FPGA Libraries
VPU	Vision Processing, Edge AI	Dedicated hardware for image and video processing. Optimized for real-time vision tasks. Low-latency execution and power-efficient design	OpenCV, TensorFlow Lite, PyTorch Mobile
DPU	Data Processing, Networking Acceleration	Specialized processing units for data-centric workloads. High throughput and low-latency data processing. Integration with networking protocols and APIs	TensorFlow, PyTorch, ONNX, DPDK
MPU	Motion Processing, Robotics	Hardware acceleration for motion tracking and control. Real-time processing of sensor data. Support for robotics frameworks and control algorithms.	ROS, MATLAB Robotics System Toolbox
QPU	Quantum Computing, Quantum Simulation	Quantum circuit processing units for quantum algorithms. Support for qubit operations and entanglement. Interface with quantum programming languages.	Qiskit, Cirq, QuTiP, Microsoft Q#

 

How do Hardware Accelerators Work? 

CPU Offload to Hardware Accelerator (FPGA). Image source:ResearchGate. 

Hardware acceleration refers to the process of shifting computing tasks from a general-purpose processor (CPU) to specialised hardware accelerators, such as field-programmable gate arrays (FPGAs) or application-specific integrated circuits (ASICs). This offload occurs because these hardware accelerators are optimised for specific tasks, making them faster and more efficient at performing those tasks compared to a CPU.

In an FPGA-based hardware accelerator architecture, the CPU communicates with the FPGA through a high-speed interface such as PCIe (Peripheral Component Interconnect Express) or AXI (Advanced eXtensible Interface).

Once an FPGA receives a task, it organises its internal logic circuits, called programmable logic blocks, to execute the task in parallel. FPGAs excel in parallel processing because of their reconfigurability, allowing them to perform multiple calculations simultaneously.

After completing the calculation, the FPGA sends the result back to the interface CPU, which has the highest speed. The FPGA’s ability to process tasks in parallel and its unique design allow it to perform tasks faster than a CPU, resulting in faster processing and reduced latency

Overall, hardware accelerations use special hardware accelerators such as FPGAs to offload specific tasks from the CPU, resulting in faster and more efficient computation, especially for tasks that require high parallelism or specialised algorithms.

 

Benefits of Hardware Accelerators: 

In the domain of AI, edge computation serves as a cornerstone in facilitating instantaneous data processing and decision-making at the network’s periphery, in close proximity to the data’s origin. Especially in computer vision, the workloads are high, and the tasks to be computed are highly data-intensive. Therefore, using AI hardware acceleration for edge devices has many advantages, including:

 

Speed and Efficiency:

AI accelerators significantly enhance the speed and performance of AI tasks such as model deployment, computer vision, and real-time inference on edge devices. They enable quicker processing of data, reducing latency, and enabling real-time use cases.

 

Promising Data Security and Privacy:

By processing critical data locally on edge devices using AI accelerators, the need to transmit sensitive information across different systems is minimized. This enhances security and allows for stricter user access control on edge devices, particularly in applications involving sensitive information like surveillance or healthcare.

 

Scalability:

Edge devices equipped with AI accelerators can scale without performance limitations. This scalability factor enables businesses to start with minimal costs and expand their AI capabilities as needed, leveraging deployment kits and development boards for efficient scaling.

 

Reliability:

AI accelerators facilitate distributed processing and storage across edge devices, reducing the impact of potential disruptions such as cyberattacks, DDoS attacks, or power outages. This distributed architecture enhances network resilience and reliability.

 

Off-net Capabilities:

Edge-based systems powered by AI accelerators can operate even in environments with limited network connectivity, ensuring continuity for mission-critical systems that require real-time processing.

Popular Deployment Kits and Development Boards:

Selecting the right edge device for model deployment is an uphill battle in computer vision and natural language processing (NLP) tasks. Here is a comprehensive comparison to guide you on the best edge device options to consider in 2024 that better suit your needs and requirements.

 

AI Performance:

Development Board	Manufacturer	AI Performance
Raspberry Pi 3B+	Raspberry Pi Ltd.	–
Raspberry Pi 4B		–
Raspberry Pi 5		–
Google Coral	coral.ai	4TOPS
Google Coral Mini
Google Coral Micro
Google SoM
Jetson AGX Orin 64GB	NVIDIA	275 TOPS
Jetson AGX Orin Industrial		248 TOPS
Jetson AGX Orin 32GB		200 TOPS
Jetson Orin NX 16GB		100 TOPS
Jetson Orin NX 8GB		70 TOPS
Jetson Orin Nano 8GB		40 TOPs
Jetson Orin Nano 4GB		20 TOPs
Jetson AGX Xavier Industrial		30 TOPS
Jetson AGX Xavier 64GB		32 TOPS
Jetson AGX Xavier
Jetson Xavier NX 16GB		21 TOPS
Jetson Xavier NX
Jetson TX2i		1.26 TFLOPS
Jetson TX2		1.33 TFLOPS
Jetson TX2 4GB
Jetson TX2 NX
Jetson Nano		472 GFLOPS

 

GPU, CPU AND RAM: 

 

Development Board	GPU Memory	RAM	Storage
Raspberry Pi 3B+	Broadcom Videocore-IV	1GB LPDDR2 SDRAM	MicroSD card slot
Raspberry Pi 4B	Broadcom VideoCore VI	2GB/4GB/8GB LPDDR4 SDRAM	MicroSD card slot
Raspberry Pi 5	Not applicable (Direct RAM access)	Varies (8GB available)	MicroSD card slot
Google Coral	Edge TPU (ML accelerator)	1GB LPDDR4 RAM	8GB eMMC storage
Google Coral Mini	Edge TPU (ML accelerator)	2GB LPDDR4 RAM	8GB eMMC storage
Google Coral Micro	Edge TPU (ML accelerator)	1GB LPDDR4 RAM	8GB eMMC storage
Google SoM	Edge TPU (ML accelerator)	8GB LPDDR4 RAM	64GB eMMC storage
Jetson AGX Orin 64GB	NVIDIA Orin GPU	64GB LPDDR4x RAM	32GB eMMC storage
Jetson AGX Orin Industrial	NVIDIA Orin GPU	64GB LPDDR4x RAM	32GB eMMC storage
Jetson AGX Orin 32GB	NVIDIA Orin GPU	32GB LPDDR4x RAM	32GB eMMC storage
Jetson Orin NX 16GB	NVIDIA Orin GPU	16GB LPDDR4x RAM	16GB eMMC storage
Jetson Orin NX 8GB	NVIDIA Orin GPU	8GB LPDDR4x RAM	16GB eMMC storage
Jetson Orin Nano 8GB	NVIDIA Orin GPU	8GB LPDDR4x RAM	MicroSD card slot
Jetson Orin Nano 4GB	NVIDIA Orin GPU	4GB LPDDR4x RAM	MicroSD card slot
Jetson AGX Xavier Industrial	NVIDIA Xavier GPU	32GB LPDDR4 RAM	32GB eMMC storage
Jetson AGX Xavier 64GB	NVIDIA Xavier GPU	64GB LPDDR4 RAM	32GB eMMC storage
Jetson AGX Xavier	NVIDIA Xavier GPU	16GB LPDDR4 RAM	32GB eMMC storage
Jetson Xavier NX 16GB	NVIDIA Xavier GPU	16GB LPDDR4 RAM	MicroSD card slot
Jetson Xavier NX	NVIDIA Xavier GPU	8GB LPDDR4 RAM	MicroSD card slot
Jetson TX2i	NVIDIA Pascal GPU	8GB LPDDR4 RAM	32GB eMMC storage
Jetson TX2	NVIDIA Pascal GPU	8GB LPDDR4 RAM	32GB eMMC storage
Jetson TX2 4GB	NVIDIA Pascal GPU	4GB LPDDR4 RAM	32GB eMMC storage
Jetson TX2 NX	NVIDIA Pascal GPU with 256 CUDA cores	4GB 128-bit LPDDR4, 1600 MHz	16GB eMMC 5.1 Flash Storage
Jetson Nano	NVIDIA Maxwell architecture with 128 CUDA cores	4GB 64-bit LPDDR4, 1600MHz	16GB eMMC 5.1

 

Connectivity, Cost, Scalability and Power: 

 

Development Board	Connectivity	Cost (Approx.)	Scalability	Power
Raspberry Pi 3B+	Gigabit Ethernet, Wi-Fi	$35	Limited	5V/2.5A DC via micro USB connector
Raspberry Pi 4B	Gigabit Ethernet, Wi-Fi, Bluetooth	Starting from $35	Moderate	5V DC via USB-C connector (minimum 3A)
Raspberry Pi 5	Gigabit Ethernet, Wi-Fi, Bluetooth	Varies based on RAM	High	5V/5A DC via USB-C
Google Coral	USB, Wi-Fi	$75	Limited	–
Google Coral Mini	USB, Wi-Fi	$99	Limited	–
Google Coral Micro	USB	$25	Limited	–
Google SoM	USB, Wi-Fi	Varies	High	–
Jetson AGX Orin 64GB	Gigabit Ethernet, Wi-Fi, Bluetooth	$1,099	High	15W – 60W
Jetson AGX Orin Industrial	Gigabit Ethernet, Wi-Fi, Bluetooth	Varies	High	15W – 75W
Jetson AGX Orin 32GB	Gigabit Ethernet, Wi-Fi, Bluetooth	$899	High	15W – 40W
Jetson Orin NX 16GB	Gigabit Ethernet, Wi-Fi, Bluetooth	$399	Moderate	10W – 25W
Jetson Orin NX 8GB	Gigabit Ethernet, Wi-Fi, Bluetooth	$349	Moderate	10W – 20W
Jetson Orin Nano 8GB	Gigabit Ethernet, Wi-Fi, Bluetooth	$199	Limited	7W – 15W
Jetson Orin Nano 4GB	Gigabit Ethernet, Wi-Fi, Bluetooth	$129	Limited	7W – 10W
Jetson AGX Xavier Industrial	Gigabit Ethernet, Wi-Fi, Bluetooth	Varies	High	20W – 40W
Jetson AGX Xavier 64GB	Gigabit Ethernet, Wi-Fi, Bluetooth	$1,099	High	10W – 30W
Jetson AGX Xavier	Gigabit Ethernet, Wi-Fi, Bluetooth	$699	High
Jetson Xavier NX 16GB	Gigabit Ethernet, Wi-Fi, Bluetooth	$399	Moderate	10W – 20W
Jetson Xavier NX	Gigabit Ethernet, Wi-Fi, Bluetooth	$349	Moderate
Jetson TX2i	Gigabit Ethernet, Wi-Fi, Bluetooth	$399	Moderate	10W – 20W
Jetson TX2	Gigabit Ethernet, Wi-Fi, Bluetooth	$399	Moderate	7.5W – 15W
Jetson TX2 4GB	Gigabit Ethernet, Wi-Fi, Bluetooth	$399	Moderate
Jetson TX2 NX	Gigabit Ethernet	$699	High
Jetson Nano	Gigabit Ethernet	$99	Moderate	5W – 10W

 

Software Frameworks and Programming Languages: 

 

Development Board	Software Frameworks	Programming Languages
Raspberry Pi 3B+	Raspbian, Ubuntu, Windows 10 IoT Core	Python, C/C++, Scratch
Raspberry Pi 4B	Raspbian, Ubuntu, Windows 10 IoT Core	Python, C/C++, Scratch
Raspberry Pi 5	Raspbian, Ubuntu, Windows 10 IoT Core	Python, C/C++, Scratch
Google Coral	TensorFlow Lite, Edge TPU API	Python, C++
Google Coral Mini	TensorFlow Lite, Edge TPU API	Python, C++
Google Coral Micro	TensorFlow Lite, Edge TPU API	Python, C++
Google SoM	TensorFlow Lite, Edge TPU API	Python, C++
Jetson AGX Orin 64GB	NVIDIA JetPack, TensorRT	Python, C++, CUDA
Jetson AGX Orin Industrial	NVIDIA JetPack, TensorRT	Python, C++, CUDA
Jetson AGX Orin 32GB	NVIDIA JetPack, TensorRT	Python, C++, CUDA
Jetson Orin NX 16GB	NVIDIA JetPack, TensorRT	Python, C++, CUDA
Jetson Orin NX 8GB	NVIDIA JetPack, TensorRT	Python, C++, CUDA
Jetson Orin Nano 8GB	NVIDIA JetPack, TensorRT	Python, C++, CUDA
Jetson Orin Nano 4GB	NVIDIA JetPack, TensorRT	Python, C++, CUDA
Jetson AGX Xavier Industrial	NVIDIA JetPack, TensorRT	Python, C++, CUDA
Jetson AGX Xavier 64GB	NVIDIA JetPack, TensorRT	Python, C++, CUDA
Jetson AGX Xavier	NVIDIA Linux for Tegra® driver package, including Ubuntu-derived sample file system	AI, Compute, Multimedia, and Graphics libraries and APIs (Refer to NVIDIA documentation for current support)
Jetson Xavier NX 16GB	NVIDIA Linux for Tegra® driver package, cloud-native support, pre-trained AI models from NVIDIA NGC and the TAO Toolkit	AI, Compute, Multimedia, and Graphics libraries and APIs (Supports all popular AI frameworks)
Jetson Xavier NX	NVIDIA Linux for Tegra® driver package, cloud-native support, pre-trained AI models from NVIDIA NGC and the TAO Toolkit	AI, Compute, Multimedia, and Graphics libraries and APIs (Supports all popular AI frameworks)
Jetson TX2i	NVIDIA Linux for Tegra® driver package	AI, Compute, Multimedia, and Graphics libraries and APIs (Refer to NVIDIA documentation for current support)
Jetson TX2	NVIDIA Linux for Tegra® driver package	AI, Compute, Multimedia, and Graphics libraries and APIs (Refer to NVIDIA documentation for current support)
Jetson TX2 4GB	NVIDIA JetPack, TensorRT	Python, C++, CUDA
Jetson TX2 NX	NVIDIA JetPack, TensorRT	Python, C++, CUDA
Jetson Nano	TensorFlow, PyTorch, Caffe, Keras, OpenCV, ROS	Python, C++

 

Recommendations:

Based on the extensive analysis offered in the article, I would propose using specific edge devices for various project scopes and requirements. For large-scale AI applications with complicated models and demanding calculations, I strongly advise using high-performance edge devices like the NVIDIA Jetson AGX Xavier or Google Coral Dev Board. These boards come with powerful AI accelerators that enable quick model deployment, rapid real-time inference, and seamless integration with AI frameworks such as TensorFlow and PyTorch. On the other hand, for smaller AI installations or development, less expensive solutions like the Raspberry Pi 4B or the NVIDIA Jetson Nano provide adequate computational power and flexibility. Furthermore, for edge computing applications in IoT contexts or embedded systems, the Raspberry Pi 3B offers cost-effective solutions with enough processing power. 

To achieve the best performance and efficiency, the edge device’s hardware specifications and functionality must be compatible with the project’s computational requirements and deployment environment.

 

Conclusion: 

This guide explains why hardware accelerators are required alongside their applications. Because in the fast-paced world of AI, you need every advantage possible. They’re the magic ingredient that makes your AI visions come to life, reducing effort and consuming less energy in the process.

To sum up, hardware accelerators, such as GPUs, are revolutionising AI capabilities in a variety of industries. AI applications are becoming more innovative and efficient thanks to these accelerators, which can improve computer vision tasks, deploy models more efficiently, and allow real-time inference on edge devices. Developers and researchers may unlock new possibilities and create meaningful AI solutions in the real world by harnessing the technical brilliance of AI-hardware accelerators, such as GPUs, and understanding their practical applications.