Applied and Computational Engineering

- The Open Access Proceedings Series for Conferences


Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

Series Vol. 4 , 31 May 2023


Open Access | Article

Artificial-Intelligence integrated circuits: Comparison of GPU, FPGA and ASIC

Yujie Wang * 1
1 Department of Electrical and Electronic Engineering, University of Bristol, Bristol, UK

* Author to whom correspondence should be addressed.

Applied and Computational Engineering, Vol. 4, 99-104
Published 31 May 2023. © 31 May 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Yujie Wang. Artificial-Intelligence integrated circuits: Comparison of GPU, FPGA and ASIC. ACE (2023) Vol. 4: 99-104. DOI: 10.54254/2755-2721/4/20230358.

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

References

1. Savekar, A., & Sachan, S. (2022). Artificial Intelligence Chip Market Size, Share | Analysis-2030. Allied Market Research. Retrieved 20 July 2022, from https://www.alliedmarketresearch.com/artificial-intelligence-chip-market

2. He, Y. (2021). Application of Artificial Intelligence in Integrated Circuits. Journal Of Physics: Conference Series, 2029(1), 012090. DOI: https://doi.org/10.1088/1742-6596/2029/1/012090

3. Li, B., Gu, J., & Jiang, W. (2019). Artificial Intelligence (AI) Chip Technology Review. 2019 International Conference On Machine Learning, Big Data And Business Intelligence (MLBDBI). DOI: https://doi.org/10.1109/mlbdbi48998.2019.00028

4. Zhang, C., Li, P., Sun, G., Guan, Y., Xiao, B., & Cong, J. (2015). Optimizing FPGA-based Accelerator Design for Deep Convolutional Neural Networks. Proceedings Of The 2015 ACM/SIGDA International Symposium On Field-Programmable Gate Arrays, 161-170. DOI: https://doi.org/10.1145/2684746.2689060

5. Nurvitadhi, E., Sheffield, D., Sim, J., Mishra, A., Venkatesh, G., & Marr, D. (2016). Accelerating Binarized Neural Networks: Comparison of FPGA, CPU, GPU, and ASIC. 2016 International Conference On Field-Programmable Technology (FPT), 77-84. DOI: https://doi.org/10.1109/fpt.2016.7929192

6. Kuon, I., & Rose, J. (2007). Measuring the Gap Between FPGAs and ASICs. IEEE Transactions On Computer-Aided Design Of Integrated Circuits And Systems, 26(2), 203-215. DOI: https://doi.org/10.1109/tcad.2006.884574

7. Wang, Y. E., Wei, G. Y., & Brooks, D. (2019). Benchmarking TPU, GPU, and CPU platforms for deep learning. arXiv preprint arXiv:1907.10701.

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9. FPGA vs. GPU Acceleration: Considering Performance/Power. Blog.bigstream.co. (2022). Retrieved 28 July 2022, from https://blog.bigstream.co/fpga-vs.-gpu-acceleration-considering-performance-and-power.

10. Singh, R. (2022). FPGA vs ASIC: Differences between them and which one to use? | Numato Lab Help Center. Retrieved 29 July 2022, from https://numato.com/blog/differences-between-fpga-and-asics/

Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 3rd International Conference on Signal Processing and Machine Learning
ISBN (Print)
978-1-915371-55-3
ISBN (Online)
978-1-915371-56-0
Published Date
31 May 2023
Series
Applied and Computational Engineering
ISSN (Print)
2755-2721
ISSN (Online)
2755-273X
DOI
10.54254/2755-2721/4/20230358
Copyright
31 May 2023
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated