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Artificial-Intelligence integrated circuits: Comparison of GPU, FPGA and ASIC
- 1 Department of Electrical and Electronic Engineering, University of Bristol, Bristol, UK
* Author to whom correspondence should be addressed.
Abstract
In the recent years, the boom in technology industries has been greatly accelerated by the development of artificial intelligence (AI). AI, which is based on machine learning (ML), can only be developed rapidly because of the continuously increasing computational capacity of AI processors. Compared to general-purpose processors (GPPs), AI processors have specially designed architectures to accelerate the operations of AI applications, such as convolution, matrix, and massive parallel computing. The objectives of this paper are: (1) to illustrate the differences between general-purpose processors and AI processors; (2) to summarise the characteristic three mainstream AI processors: GPU, FPGA and ASIC, and draw a comparison among them. It shows that GPUs provide very competitive performance with high power consumption; FPGAs can offer high efficiency at low cost; and AISCs provide the highest performance with the lowest power consumption, but cost the most.
Keywords
AI, Integrated Circuits, GPU, FPGA, ASIC
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Cite this article
Wang,Y. (2023). Artificial-Intelligence integrated circuits: Comparison of GPU, FPGA and ASIC. Applied and Computational Engineering,4,99-104.
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Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning
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