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What Makes A Good AI Accelerator

The dynamic nature and rapid growth of machine learning algorithms and AI have created the need to develop accelerators that are optimized to handle different data types. In the past, having one processor for the general purpose was considered enough, but now there are dozens fighting on the market. But what is an accelerator and how does it benefit AI? 

What Is an AI Accelerator?

During the 1980s, graphical accelerators made PCs quicker and more efficient by freeing up the main processor and taking care of all the graphics needs. Similarly, AI accelerators relieve the main processor of the burden of dealing with resource-intensive AI tasks.

An AI accelerator is a piece of technology that aids in the training of deep learning models by speeding up the process. They’re utilized to boost the performance of AI applications and reduce training time. Their goal is to create a higher-performance infrastructure for deep learning applications to run faster. They should be employed because they can help accelerate deep learning applications by providing significant performance benefits.

The overall goal is to process algorithms quicker than ever before while utilizing the least amount of power possible, whether at the edge, in the data center, or somewhere in between. However, because machine learning algorithms are outpacing the technology, accelerator architectures are all over the place.

Different Types of Hardware Accelerators

As deep learning and artificial intelligence tasks became more popular over the last decade, specialized hardware units were designed or adapted from existing products to speed up these tasks, as well as to have parallel high-throughput systems that are targeted at various applications, such as neural network simulations.

Hardware acceleration has a number of advantages, the most important of which is speed. Accelerators can shorten the time it takes to train and run an AI model in half, and they can also be used to perform unique AI-based activities that can’t be done on a CPU. Let’s take a look at the most common hardware AI accelerators.

Graphics Processing Unit (GPU)

A graphics processing unit (GPU) is a specialized processor that can perform fast processing, particularly for image rendering. They’ve become an essential component of modern supercomputing. They’ve been utilized to build new hyper-scale data centers and have evolved into accelerators, accelerating a variety of operations ranging from encryption to networking to artificial intelligence. GPUs have triggered an AI revolution, have become an integral feature of current supercomputers, and continue to propel gaming and professional graphics forward.

Unit for Vision Processing (VPU)

A vision processing unit (VPU) is a new form of microprocessor and an AI accelerator designed to speed up machine vision activities. According to reports, the vision processing unit is more suited to conducting various machine vision algorithms. These technologies are created for parallel processing and may be designed with specific resources for gathering visual data from cameras. Some of these tools are low-power, high-performance devices that can be plugged into programmable interfaces.

Field-Programmable Gate Array (FPGA)

A field-programmable gate array (FPGA) is an integrated circuit (IC) that may be customized after manufacture by a client or a designer, hence the name “field-programmable.” FPGAs are made up of a hierarchy of programmable logic blocks and “reconfigurable interconnects” that allow the blocks to be joined together like numerous logic gates in various configurations.

To implement complicated data computations, today’s field-programmable gate arrays (FPGAs) feature a large number of logic gates and RAM blocks. FPGAs are a great fit for a variety of markets because of their programmability. After production, FPGAs can be reprogrammed to meet the desired application or functionality requirements. This distinguishes FPGAs from Application-Specific Integrated Circuits (ASICs), which are made specifically for specific design needs.

Integrated Circuits for Specific Applications (ASIC)

With something called an application-specific integrated circuit, a new category of AI hardware accelerator is gaining traction (ASIC). ASICs use tactics like optimized memory usage and lower precision arithmetic to speed up calculation and boost computing throughput. Half-precision and the bfloat16 floating-point format are two of the low-precision floating-point formats that have been embraced and used AI accelerations. In an AI workflow, hardware acceleration is employed to speed up the computing processes.

Unit for Tensor Processing (TPU)

A tensor processing unit (TPU) is a specialized circuit that implements all of the control and arithmetic logic required to run machine learning algorithms, usually on predictive models such as artificial neural networks (ANNs) or random forests (RFs).

Tensors are multi-dimensional arrays or matrices that can carry data points in a row and column format, such as the weights of a node in a neural network. Tensors are used to do basic calculations. TPUs were used in the well-known DeepMind’s AlphaGo, in which AI defeated the top Go player in the world. 

How Do Accelerators Help Artificial Intelligence?

What is an accelerator exactly? The most common definition is that it is a hardware device or a software program that controls and enhances a computer’s performance. Accelerators help boost a computer’s performance, but there is a wide range of accelerators that are available. 

In terms of AI, you can find an accelerator to specifically handle all AI requirements and boost performance. Wikipedia defines an AI accelerator as a “class of a specialized hardware accelerator or even a computer system that was designed to accelerate artificial intelligence applications, in particular machine learning, machine vision, and artificial neural networks. But what is it really? 

It is a computer system or specialized hardware that was specifically designed to accelerate artificial intelligence apps, especially machine learning, artificial neural networks, robotics, and any data-intensive or sensor-driven tasks that need to be executed. 

As artificial intelligence and deep learning tasks and workload have grown in the last years, there have been specialized hardware units that were needed to accelerate these tasks with parallel high-throughput systems for workstations that are targeted at different applications.