Research Article
Open access
Published on 25 March 2024
Download pdf
Lei,W. (2024). Revolutionizing machine learning: A comprehensive analysis of ASIC hardware accelerators and their applications. Applied and Computational Engineering,51,152-157.
Export citation

Revolutionizing machine learning: A comprehensive analysis of ASIC hardware accelerators and their applications

Weiming Lei *,1,
  • 1 Huazhong University of Science and Technology

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/51/20241208

Abstract

The growth of the web over the past few years has led to tremendous data growth, which has provided a powerful impetus for artificial intelligence and machine learning. Machine learning algorithms are widely used in various classification and prediction problems. However, with the rich data types and needs, the traditional software computing method that relies on CPU can no longer meet the application scenarios under different requirements. Machine Learning (ML) hardware accelerators, especially Application Specific Integrated Circuits (ASICs), have become trendy to meet a variety of needs. This paper reviews the research on ML ASIC in the past few years, reviews the development of ML and ASIC design, and summarizes the characteristics of their use. It is followed by examples of various scenarios for which it can be used, such as medical diagnostics and internet of thing (IOT) terminals. Finally, the author analyses the existing problems and limitations, and gives the improvement methods of hardware and algorithm to deal with the related obstacles. It is hoped that this paper can provide some help and convenience for the subsequent related research.

Keywords

ASIC; Machine Learning; Hardware Accelerators

[1]. Fradkov A L. Early history of machine learning. IFAC-PapersOnLine, 2020, 53(2): 1385-1390.

[2]. Zhang L, Tan J, Han D, et al. From machine learning to deep learning: progress in machine intelligence for rational drug discovery. Drug discovery today, 2017, 22(11): 1680-1685.

[3]. Plasek A. On the cruelty of really writing a history of machine learning. IEEE Annals of the History of Computing, 2016, 38(4): 6-8.

[4]. Çelik Ö. A research on machine learning methods and its applications. Journal of Educational Technology and Online Learning, 2018, 1(3): 25-40.

[5]. Basiladze S G. Application specific integrated circuits for ionizing-radiation detectors (review, part 1). Instruments and Experimental Techniques, 2016, 59: 1-52.

[6]. Piso D, Veiga-Crespo P, Vecino E. Modern monitoring intraocular pressure sensing devices based on application specific integrated circuits. 2012.

[7]. Talib M A, Majzoub S, Nasir Q, et al. A systematic literature review on hardware implementation of artificial intelligence algorithms. The Journal of Supercomputing, 2021, 77: 1897-1938.

[8]. Akhoon M S, Suandi S A, Alshahrani A, et al. High performance accelerators for deep neural networks: A review. Expert Systems, 2022, 39(1): e12831.

[9]. Abubakar S M, Yin Y, Tan S, et al. A 746 nW ECG Processor ASIC Based on Ternary Neural Network. IEEE Transactions on Biomedical Circuits and Systems, 2022, 16(4): 703-713.

[10]. Sudha M. Evolutionary and neural computing based decision support system for disease diagnosis from clinical data sets in medical practice. Journal of medical systems, 2017, 41(11): 178.

[11]. Taştan İ, Karaca M, Yurdakul A. Approximate CPU design for IoT end-devices with learning capabilities. Electronics, 2020, 9(1): 125.

[12]. Lin Kaiwen. An Low power ECG Processor with Weak-Strong Hybrid Classifier for Arrhythmia Detection. MS thesis. Zhejiang University. 2018.

[13]. Sun Y, Cheng A C. Machine learning on-a-chip: A high-performance low-power reusable neuron architecture for artificial neural networks in ECG classifications. Computers in biology and medicine, 2012, 42(7): 751-757.

[14]. Fan Fangwen. Study on Application of reusable ANN node IP in IoT. MS thesis. Beijing University of Technology. 2018.

[15]. Liu Qingsong. Neural Network based Low Power Keyword Spotting Hardware Design. MS thesis. University of Electronic Science and Technology of China. 2022.

[16]. Sarić R, Chen J, Čustović E, et al. Design of ASIC and FPGA system with Supervised Machine Learning Algorithms for Solar Particle Event Hourly Prediction. IFAC-PapersOnLine, 2022, 55(4): 230-235.

[17]. Miryala S, Mittal S, Ren Y, et al. Waveform processing using neural network algorithms on the front-end electronics. Journal of Instrumentation, 2022, 17(01): C01039.

[18]. Borrego-Carazo J, Castells-Rufas D, Biempica E, et al. Resource-constrained machine learning for ADAS: A systematic review. IEEE Access, 2020, 8: 40573-40598.

Cite this article

Lei,W. (2024). Revolutionizing machine learning: A comprehensive analysis of ASIC hardware accelerators and their applications. Applied and Computational Engineering,51,152-157.

Data availability

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

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About volume

Volume title: Proceedings of the 4th International Conference on Signal Processing and Machine Learning

Conference website: https://www.confspml.org/
ISBN:978-1-83558-347-0(Print) / 978-1-83558-348-7(Online)
Conference date: 15 January 2024
Editor:Marwan Omar
Series: Applied and Computational Engineering
Volume number: Vol.51
ISSN:2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).