AI Hardware Accelerators For Machine Learning And Deep Learning | How To Choose One

What is an AI Accelerator?

Machine Learning(ML), particularly its subfield, Deep Learning, mainly consists of numerous calculations involving Linear Algebra like Matrix Multiplication and Vector Dot Product. AI accelerators are specialized processors designed to accelerate these core ML operations, improve performance and lower the cost of deploying ML-based applications. AI accelerators can significantly reduce the time to train and execute an AI model and perform specific AI tasks that cannot be conducted on a CPU.

The main aim of AI accelerators is to minimize power while doing calculations. These accelerators use strategies like optimized memory use and low-precision arithmetic to accelerate computation. AI accelerators take an algorithmic approach to match specific tasks for dedicated problems.

AI accelerators’ location (server/data centers or Edge) is also key to processing their functionality. Data centers provide more computational power, memory, and communication bandwidth, whereas Edge is more energy efficient.

What are the different types of hardware AI Accelerators?

• Graphics Processing Units (GPU)

They are primarily used for rendering images, capable of doing rapid processing. Their highly parallel structures allow them to handle multiple pieces of data simultaneously, unlike CPUs, which work with data in a serialized manner involving a lot of switching between different tasks. This makes GPUs suitable for accelerating matrix-based operations involved in deep learning algorithms.

• Application-Specific Integrated Circuits (ASIC)

 They are specialized processors built for computing deep-learning inferences. They use low-precision arithmetic to speed up the computing process in an AI workflow. Compared to general-purpose processors, they are more performant and cost-effective. A great example of ASIC is Tensor Processing Units (TPU) which Google initially designed for use in its data center. TPUs were utilized in DeepMind’s AlphaGo, where AI defeated the best Go player in the world. 

• Vision Processing Unit (VPU)

VPU is a microprocessor intended to accelerate computer vision tasks. While GPUs focus on performance, VPUs are optimized for performance per watt. They are fit for performing algorithms like Convolutional Neural Networks (CNN), Scale-invariant feature transform (SIFT), etc. The target market of VPUs includes Robotics, Internet of Things, smart cameras, and integrating computer vision acceleration into smartphones.

• Field-Programmable Gate Array (FPGA)

It is an integrated circuit that has to be configured by the customer or a designer after manufacturing, hence the name “field-programmable.” They include a series of programmable logic blocks that can be configured to perform complex functions or act as logic gates. FPGAs can perform various logical functions simultaneously, but they are considered unsuitable for technologies like self-driving cars or deep learning applications.

What is the need for an AI accelerator for Machine Learning inference?

Using AI accelerators for Machine Learning inference has many advantages. Some of them are mentioned below:

• Speed and performance: AI accelerators lower the latency of the time it takes to answer a question and are valuable for safety-critical applications.
• Energy efficiency: AI accelerators are 100-1000x more efficient than general-purpose computing machines. Neither do they draw too much power nor dissipate too much heat while performing voluminous amounts of calculations.
• Scalability: With AI accelerators, the problem of parallelizing an algorithm along multiple cores can be easily solved. Accelerators make it possible to achieve a speed enhancement level equal to the number of cores involved.
• The heterogeneous architecture of AI accelerators allows a system to accommodate multiple specialized processors to achieve the computational performance required by an AI application.

How to choose an AI hardware accelerator?

There is no single correct answer to this question. Different types of accelerators are suitable for different kinds of tasks. For example, GPUs are great for “cloud” related tasks like DNA sequencing, whereas TPUs are better for “edge” computing, where the hardware should be small, power-efficient, and low-cost. Other factors like latency, batch size, cost, and type of network also determine the most suitable hardware AI accelerator for a particular AI task.

Different types of AI accelerators tend to complement each other. For example, a GPU can be used to train a neural network, and inferences can be run using a TPU. Moreover, GPUs tend to be universal – any TensorFlow code can be run with them. In contrast, TPUs require compiling and optimization, but the complex structure of a TPU allows it to execute codes efficiently.

FPGAs are more advantageous than GPUs in terms of flexibility and improved integration of programmable logic with the CPU. Conversely, GPUs are optimized for parallel processing of floating-point operations using thousands of small cores. They also provide great processing options with higher energy efficiency.

The computing power required to use machine learning is much greater than anything else we use computer chips for. This power demand has created a booming market for AI chip startups and helped double venture capital investment over the past five years.

Global sales of AI chips grew 60% last year to $35.9 billion, with roughly half of that coming from specialized AI chips in mobile phones, according to data from PitchBook. The market is predicted to grow at over 20% annually, reaching around $60 billion by 2024.

The growth and expansion of AI workloads have allowed startups to develop purpose-built semiconductors to meet their needs better than general-purpose devices. Some startups manufacturing such chips include Hailo, Syntiant, and Groq. Hailo introduced a processor, Hailo-8, capable of performing 26 tera operations per second with 20 times less power consumption than Nvidia’s Xavier processor.

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