AI Hardware Accelerators for Machine Learning and Deep Learning | How to choose one


What is an AI Accelerator?

Machine Learning (ML), in particular its subfield, Deep Learning, consists mainly of many calculations involving Linear algebra As Matrix multiplication and Vector dot product. AI accelerators are specialized processors designed to speed up these basic ML operations, improve performance, and reduce the cost of deploying ML-based applications. AI accelerators can significantly reduce the training and execution time of an AI model and perform specific AI tasks that cannot be performed on a CPU.

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

Location of AI accelerators (server/data centers Where Edge) is also essential to processing their functionality. Data centers provide more computing power, memory and communication bandwidth, while Edge is more energy efficient.

What are the different types of hardware AI accelerators?

  • Graphics Processing Units (GPUs)

They are mainly used for rendering images, capable of fast processing. Their highly parallel structures allow them to handle multiple pieces of data simultaneously, unlike CPUs, which work with data in a serialized fashion involving many switches between different tasks. This makes GPUs suitable for accelerating matrix operations involved in deep learning algorithms.

  • Application-specific integrated circuits (ASICs)

They are specialized processors designed to compute deep learning inferences. They use low-precision arithmetic to speed up the calculation process in an AI workflow. Compared to general-purpose processors, they are more efficient and more economical. A good example of an ASIC is Tensor Processing Units (TPU) who Google originally designed for use in its data center. TPUs have been used in DeepMind Alpha Gowhere the AI ​​beat the best Go player in the world.

  • Vision Processing Unit (VPU)

VPU is a microprocessor intended to speed up computer vision tasks. While GPUs focus on performance, VPUs are optimized for performance per watt. They are adept at running algorithms such as Convolutional Neural Networks (CNN), Scale invariant feature transformation (SIFT), etc. The target market for VPUs includes robotics, the Internet of Things, smart cameras, and the integration of computer vision acceleration into smartphones.

  • Field Programmable Gate Array (FPGA)

It is an integrated circuit that must be configured by the customer or a designer after manufacture, 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 logic functions simultaneously, but they are considered unsuitable for technologies such as 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 benefits. Some of them are mentioned below:

  • Speed ​​and performance: AI accelerators reduce the latency of the time it takes to answer a question and are valuable for safety-critical applications.
  • Energetic efficiency : AI accelerators are 100 to 1000 times more efficient than general purpose computing machines. They do not consume too much power and do not dissipate too much heat while performing large calculations.
  • Scalability: With AI accelerators, the problem of parallelizing an algorithm across multiple cores can be easily solved. Accelerators achieve a level of speed improvement equal to the number of cores involved.
  • heterogeneous architecture AI accelerators allow 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 one 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 for “edge” computing, where hardware needs to be small, power-efficient, and low-power. expensive. Other factors such as latency, batch size, cost, and network type also determine the most appropriate 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 performed using a TPU. Also, GPUs tend to be universal – any TensorFlow code can be run with them. In contrast, TPUs require compilation 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 offer excellent treatment options with higher energy efficiency.

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

Global AI chip sales grew 60% last year to $35.9 billionabout half of which comes from specialized AI chips in mobile phones, according to data from PitchBook. The market is expected to grow by more than 20% per year, reaching approximately $60 billion by 2024.

The growth and expansion of AI workloads has allowed startups to develop purpose-built semiconductors to better suit their needs than general-purpose devices. Some startups making such chips include Hailo, Syntiantand Groq. Hailo presented a processor, Hailo-8, capable of performing 26 tera operations per second with 20 times lower power consumption than Nvidia xavier processor.

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I am a civil engineering graduate (2022) from Jamia Millia Islamia, New Delhi and have a keen interest in data science especially neural networks and their application in various fields.


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