AI accelerators are specialized hardware accelerators and coprocessors that process artificial intelligence algorithms or AI-related tasks. The expansion of AI applications and use cases has made these hardware components advantageous in modern consumer electronic devices such as computers, smartphones, and other smart devices. These hardware components are also found in autonomous or self-driving vehicles, robotics systems, and automated machines used in different industrial applications.
Remember that AI accelerators are coprocessors. Their purpose is to take AI-related workloads from the central processor to improve the efficiency of the overall system. Examples of these workloads include machine learning, deep learning, image processing, and natural language processing, among others. Their purpose and advantages also rest on the purpose and advantages of hardware accelerators. Of course, to understand better their purpose, it is important to understand the different types of AI accelerators.
A Guide to the Different Types of AI Accelerators
1. Graphics Processing Unit
A graphics accelerator or a graphics processing unit or GPU is a hardware accelerator or specialized coprocessor designed to handle image rendering. These components are essential in modern computer systems with a graphical user interface.
Most personal computers and portable devices such as smartphones and tablets have integrated graphics processors that form part of their respective system-on-chips. Use cases that require intensive image rendering such as in the case of video gaming and high-resolution graphics design and video editing require discrete graphics processors.
Nevertheless, behind rendering images or processing graphics, GPUs are flexible. Note that these components have been used in blockchain applications that require proof-of-work validation to mine cryptocurrencies or validate blockchain entries.
GPUs have also been the first coprocessors that have been retrofitted as AI accelerators. They have been used for AI-related processing because the mathematical bases of image manipulation and artificial neural networks and relevant deep learning models are similar. Data centers used for training AI models with huge datasets use discrete GPUs.
2. Field-Programmable Gate Arrays
A field-programmable gate array or FPGA is an integrated circuit that can be programmed in the field after it has been manufactured. Traditional microprocessors have fixed architectures while FPGAs can be programmed to a particular architecture.
FPGAs can be described as a blank canvas. They can be configured to perform a wide range of digital logic functions. The configuration is generally specified using a hardware description language. They are often used in applications that require high-performance and low-latency processing. These include video processing, simulations, and cryptography.
There have been attempts to configure FPGAs as dedicated AI accelerators. They have been used for handling machine learning and deep learning tasks that require complex mathematical computations. They are also used for implementing neural networks, providing real-time video processing, and can be employed in computer vision applications.
Remember that FPGAs are beneficial for use cases that require low latency and high performance. The search engine Bing uses these chips for its search algorithm while Microsoft uses them in its Project Catapult which is aimed at augmenting cloud computing.
3. Application-Specific Integrated Circuit
Running in contrast with FPGAs are application-specific integrated circuits or ASICs. They are integrated chips designed and deployed for a particular use. Remember that FPGAs are fundamentally blank chips manufactured without a particular use case in mind. ASICs are manufactured for a specific application.
ASICs are essentially custom-built designed and optimized for a specific task or set of tasks. They can be a standalone hardware component or a part of a greater integrated chip such as in the case of a system-on-a-chip composed of different processors and coprocessors.
Nevertheless, in the field of artificial intelligence, ASICs are useful in AI-related tasks where high computational power is required. Examples include deep learning algorithms and large language models or real-time video and image processing such as in the case of computational photography features of smartphones.
There are several examples of ASICs. Google began using its Tensor Processing Unit or TPU in 2015 and made it available to the public in 2018. A TPU is used for neural network machine learning to perform matrix computations.
Another example is the Neural Engine of Apple. It is an AI accelerator built within the A series and M series system-on-chips used in iPhones, iPads, and Mac computers. The AI Engine of Qualcomm is another example that is based on its proprietary Qualcomm Hexagon digital signal processor and a licensed Tensor accelerator.
4. Massively Multicore Scalar Processors
A particular massively multicore scalar processor is essentially a multi-core processor. Modern CPUs and GPUs are multi-core processors. However, based on its name alone, a massively multicore scalar processor has massive amounts of simple processing cores.
Scalar processing is at the heart of this hardware accelerator. A scalar processor processes a single data item at a time. However, considering its multiple cores, a massively multicore scalar processor can execute more than one data item or multiple instructions in a single clock cycle by distributing them in its redundant cores.
Massively multicore scalar processors are also called superscalar processors. One of the advantages of these hardware components is that they use simple arithmetic units that can be combined in various ways to execute different types of algorithms.
Another advantage of massively multicore scalar processors is that they are highly scalable. This enables them to handle complex computations effectively and efficiently. Hence, in considering this, this advantage also makes them suitable for AI applications where large amounts of data need to be processed as quickly and as power efficiently as possible.
5. Neuromorphic Hardware Components
The emerging field of neuromorphic computing is dedicated to developing approaches to computing patterned from the structure and function of the human brain or biological neural systems. Neuromorphic hardware is one of its applications
A neuromorphic hardware is designed to mimic the features of the human brain. For example, considering how neurons and synapses interact in a biological neural system, this hardware has structures and components that allow the simulation of neurological electrical activity. This can also be called a physical artificial neural network.
There are different benefits to using this hardware. It is designed to effectively and efficiently process and handle complex and high-dimensional data. This makes it suitable for use in AI applications such as natural language processing and computer vision.
Using a particular neuromorphic hardware in a computer system would make it an AI accelerator and a coprocessor because it will be dedicated to handling AI-specific applications. This same hardware can also become a next-generation main processor or central processing unit of a computer system based on neuromorphic computing.