What is an artificial intelligence accelerator?
Machine learning (ML), and in particular its subfield, deep learning, mainly consists of many computations that include linear algebra Such as matrix multiplication And the vector raster product. AI accelerators are specialized processors designed to speed up these core ML processes, improve performance and lower the cost of deploying ML-based applications. AI accelerators can significantly reduce the time required to train and execute an AI model and perform specific AI tasks that cannot be performed on a CPU.
The main goal of AI accelerators is to reduce power while performing calculations. These accelerators use strategies such as Optimized memory usage And the low precision arithmetic to speed up the calculation. AI accelerators take a computational approach to matching specific tasks to custom problems.
artificial intelligence accelerators websiteServers / Data Centers or edge) is also a key to manipulating their functions. Data centers provide more computing power, memory, and connection bandwidth, while edge It is more energy efficient.
What are the different types of hardware AI accelerators?
- Graphics Processing Units (GPUs)
It is mainly used to display images, and is able to do fast processing. Their highly parallel structures allow for handling multiple pieces of data simultaneously, unlike CPUs, which work with data in a sequential manner that involves a lot of switching between different tasks. This makes GPUs suitable for accelerating matrix-based operations embedded 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 computing in an AI workflow. Compared to general-purpose processors, it is more performance and cost-effective. A great example of ASIC is Tensioner Processing Units (TPU) Which The Google It was initially designed for use in its own data center. TPUs were used in DeepMind’s AlphaGowhere artificial intelligence defeated the best player in the world.
- Vision Processing Unit (VPU)
VPU is a microprocessor intended to speed up computer vision tasks. While GPUs focus on performance, CPUs are optimized for performance per watt. They are fit to perform algorithms like convolutional neural networks (CNN) , Fixed scale feature conversion (SIFT), etc. The target market for VPUs includes robotics, IoT, smart cameras and 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 designer after manufacture, hence the name “programmable domain”. It includes 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 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 advantages. Some of them are mentioned below:
- speed and performance: AI accelerators reduce the response time of the time taken to answer a question and are valuable for safety-critical applications.
- Energy Efficiency: AI accelerators are 100-1000 times more efficient than general-purpose computing machines. It does not consume much energy and does not dissipate much heat while performing huge calculations.
- Scalability: Using AI accelerators, the problem of algorithm parallelization along multiple cores can be easily solved. Accelerators make it possible to achieve a level of speed improvement equal to the number of cores involved.
- heterogeneous architecture of artificial intelligence accelerators that allow the system to accommodate multiple specialized processors to achieve the computational performance required by the application of artificial intelligence.
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 CPUs are better for “edge” computing, where hardware needs to be small, energy efficient, and low-cost. 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 integrate with each other. For example, a GPU can be used to train a neural network, and inferences can be run with a TPU. Furthermore, GPUs tend to be universal – any TensorFlow code can run with them. In contrast, TPUs require assembly and optimization, but the complex structure of the TPU allows it to execute codes efficiently.
FPGAs are more useful than GPUs in terms of flexibility and improved integration of programmable logic with the CPU. On the contrary, GPUs are optimized for parallel processing of floating point operations using thousands of micro-cores. It also offers great processing options with higher energy efficiency.
The computing power required to use machine learning is far greater than anything else we use computer chips for. This energy demand has created a thriving market for AI chip startups and helped Double Investing venture capital over the past five years.
Global sales of artificial intelligence chips grew by 60% last year to $35.9 billionwith about half of that coming from AI chips specializing in mobile phones, according to data from stadium book. The market is expected to grow more than 20% annually, reaching about 60 billion dollars by 2024.
The growth and expansion of AI workloads has allowed startups to develop semiconductors tailored to better meet their needs than general-purpose devices. Some of the startups that manufacture such chips include HelloAnd the syntheticAnd the puppy. Hailo introduced a healer, Hailo-8, Capable of 26 tera operations per second with 20 times less power consumption than Nvidia Xavier Healer.
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