April 04, 2021 by Emmanuel Ikimi
Understanding the role of AI accelerators in artificial intelligence gives an insight into its benefits in the world of electronics and computing, particularly as our collective interests in human-level intelligence increase. We look at the technology and how it may benefit engineers.
AI accelerators are hardware chips specifically designed to accelerate and facilitate machine learning computations. In the bid to utilising artificial intelligence to its full potential at minimal power requirements, AI accelerators can be incorporated in machines to improve basic machine computations, enhance system performance, and all while minimising latency. To achieve this, AI accelerators have been utilised to accelerate the analysis of large linear algebra computations and parallelised them into smaller, often identical parts, some of which operate simultaneously.
Some applications of artificial intelligence include the Internet of Things, algorithms for guiding robotic decision-making, and other data-intensive tasks.
The main purpose of AI accelerators is to analyse algorithms faster using minimal power. Computer scientists have developed AI accelerators to take an algorithmic approach to match specific tasks or dedicated problems. The location of AI accelerators and compute architecture is of course key to processing functionality. The significant points at which AI accelerators are put in place include the data centre and the edge. Data centres demand a scalable computing infrastructure. They compute, network, and store critical data.
We discussed in a previous article one of the largest AI accelerator chips ever designed for data centers: the Wafer-Scale Engine. It accelerates AI research by providing more memory and communication bandwidth. The edge is the opposite. Here, energy consumption is critical as well as space utilisation. AI accelerator IP, or intellectual property, is built into edge SoC devices that are incredibly thin, yet produce near-instantaneous results.
Artificial intelligence concept. Pictured: the term 'AI' in the palm of a user's hand, which is surrounded by graphics related to intelligent technology (such as robotics)
Image credit: Bigstock
To address the increasing workload required by deep learning and machine learning, the last decade has seen the development of specialised hardware chips to minimise the demands put on processors. and subsequently led to the creation of various hardware AI accelerators. This hardware greatly minimises the time needed to both develop and operate AI systems.
Some of the most popular hardware AI accelerators include the following:
GPUs (graphics processing units): GPUs are dedicated chips that enable rapid processing (mostly in terms of image rendering).
VPUs (vision processing units): VPUs are suited to running computer vision algorithms. They collect visual data from cameras and enable parallel processing.
FPGAs (field-programmable gate arrays): FPGA are embedded systems that are designed to be configured by the consumer or the producer, post-development.
Application-specific Integrated Circuits (ASICs): ASICs use techniques such as enhanced memory usage and low-precision arithmetic to improve computation and maximise computational throughput.
Scale optimisation and speed are crucial focal points in the field of AI systems, and AI accelerators play a major role in ensuring that such applications achieve relatively fast results. Just some of the benefits are outlined in the following subsections.
AI accelerators are of course more powerful than general-purpose computing devices, but they nevertheless require minimal energy to run. This is particularly important, especially in data centres where heat generation, caused by inefficient processing, could otherwise cause major damage to computing operations.
Thanks to the pace at which they function, AI accelerators ensure low-latency computation, due to the efficiency of their response times. This is crucial in time-critical areas, such as advanced driver assistance, where real-time data is needed to ensure the safety of human lives.
Developing algorithms to solve any problem is tasking. Implementing this algorithm and paralleling it through several cores with higher computing capability is much more difficult still.
In the neural network environment, AI accelerators make it feasible to reach a degree of speed and efficiency that can be approximately equal to the number of cores involved. The key is in the scalability of the accelerators.
There are several fascinating applications in which artificial intelligence and machine learning are used behind the scenes to affect our daily lives. Consider, for instance, that AI accelerators have been employed in public safety and security, which involves such technologies as autonomous drones, facial recognition systems, and security cameras.
To help accommodate such a demand on AI systems, engineers can utilise AI accelerators to ensure that all manner of modern technology can achieve real-time data. And there is no understating the importance of this, as artificial intelligence becomes increasingly prevalent—not just at the general consumer level—but in mission-critical systems, too.
