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

What is an AI accelerator?

Machine learning (ML), especially its sub-field deep learning, consists mainly of numerous calculations Linear Algebra how Matrix multiplication and vector dot product. AI accelerators are specialized processors designed to speed up these core ML operations, improving performance and reducing the cost of deploying ML-based applications. AI accelerators can significantly reduce the time to train and run an AI model and perform certain AI tasks that cannot be performed on a CPU.

The main goal of AI accelerators is to minimize power consumption during calculations. These accelerators use strategies like optimized memory usage and Low-precision arithmetic speed up calculation. AI accelerators take an algorithmic approach to matching specific tasks to dedicated problems.

Location of AI accelerators (Servers/Data Centers or edge) is also key to processing their functionality. Data centers, on the other hand, offer more computing power, storage and communication bandwidth edge is more energy efficient.

What Are the Different Types of Hardware AI Accelerator?

• Graphics processing units (GPU)

They are mainly used to render images that allow for fast processing. Their highly parallel structures allow them to process multiple data at the same time, unlike CPUs that work with data serialized, which requires many switches 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 to compute deep learning inference. They use low-precision arithmetic to speed up the computational process in an AI workflow. Compared to general purpose processors, they are more powerful and less expensive. A great example of ASIC is Tensor Processing Units (TPU) the Google originally designed for use in his data center. TPUs were used in DeepMind AlphaGowhere AI defeated the best Go player in the world.

• Image Processing Unit (VPU)

VPU is a microprocessor designed to speed up computer vision tasks. While GPUs focus on performance, VPUs are optimized for performance per watt. They are suitable for performing algorithms such as Convolutional Neural Networks (CNN), Scale-invariant feature transformation (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 must be configured after manufacture by the customer or a designer, hence the name “field programmable”. They comprise a series of programmable logic blocks that can be configured to perform complex functions or act as logic gates. FPGAs can run various logical functions simultaneously, but are considered unsuitable for technologies such as self-driving cars or deep learning applications.

Why is an AI accelerator needed for machine learning inference?

Using AI accelerators for machine learning inference has many benefits. Some of them are mentioned below:

• Speed ​​and Performance: AI accelerators reduce the latency for answering a question and are valuable for safety-critical applications.
• Energy efficiency: AI accelerators are 100-1000 times more efficient than general purpose calculators. They don’t use too much power, nor give off too much heat while doing extensive calculations.
• Scalability: With AI accelerators, the problem of parallelizing an algorithm across multiple cores can be easily solved. Accelerators make it possible to achieve an increase in speed commensurate with the number of cores involved.
• The heterogeneous architecture AI accelerators allow a system to accommodate multiple specialized processors to achieve the computing power required for 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 types of tasks. For example, GPUs are great for “cloud”-related tasks like DNA sequencing, while TPUs are better suited for “edge” computing, where the hardware should be small, power-efficient, and inexpensive. Other factors such as latency, batch size, cost, and type of network will also determine the most appropriate hardware AI accelerator for a given 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 inference can be performed with a TPU. Additionally, GPUs tend to be universal – any TensorFlow code can run on them. In contrast, TPUs need to be compiled and optimized, but the complex structure of a TPU allows code to run 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 with thousands of small cores. They also offer great processing options with higher energy efficiency.

The computing power required for machine learning is far greater than anything else we use computer chips for. This power demand has created and contributed to a booming market for AI chip startups double Venture capital investments in the last five years.

Global sales of AI chips grew 60% last year $35.9 billionwith about half of that coming from specialized AI chips in cellphones, according to the data PitchBook. The market is forecast to grow at over 20% annually, which is a reach of about 60 billion dollars until 2024.

The growth and expansion of AI workloads has enabled startups to develop purpose-built semiconductors that better meet their needs than general-purpose devices. Some startups making such chips include Hi, Syntheticand groq. Hailo presented a processor hailo-8, capable of 26 tera operations per second with 20 times less power consumption than Nvidia xavier Processor.

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