A Survey on FPGA Electronic Design Automation Technology Based on Machine Learning

TIAN Chunsheng; CHEN Lei; WANG Yuan; WANG Shuo; ZHOU Jing; PANG Yongjiang; DU Zhong

doi:10.11999/JEIT220183

Volume 45 Issue 1

Jan. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(1): 1-13

TIAN Chunsheng, CHEN Lei, WANG Yuan, WANG Shuo, ZHOU Jing, PANG Yongjiang, DU Zhong. A Survey on FPGA Electronic Design Automation Technology Based on Machine Learning[J]. Journal of Electronics & Information Technology, 2023, 45(1): 1-13. doi: 10.11999/JEIT220183

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TIAN Chunsheng, CHEN Lei, WANG Yuan, WANG Shuo, ZHOU Jing, PANG Yongjiang, DU Zhong. A Survey on FPGA Electronic Design Automation Technology Based on Machine Learning[J]. Journal of Electronics & Information Technology, 2023, 45(1): 1-13. doi: 10.11999/JEIT220183

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A Survey on FPGA Electronic Design Automation Technology Based on Machine Learning

doi: 10.11999/JEIT220183

1.
School of Integrated Circuits, Peking University, Beijing 100871, China
2.
Beijing Microelectronics Technology Institute, Beijing 100076, China

Funds: The National Natural Science Foundation of China (U20A20204), The National Key S&T Special Projects (2009ZYHJ0005)

Received Date: 2022-02-25
Accepted Date: 2022-08-02
Rev Recd Date: 2022-07-22

Available Online: 2022-08-04

Publish Date: 2023-01-17

Abstract

Abstract

With the advent of the post-Moore era, Field Programmable Gate Array (FPGA) is widely used in various fields such as Internet of Things (IoTs), 5G communication, aerospace, weapons and equipment because of its flexible repetitive programmable characteristics and low development cost. As a necessary means in the process of FPGA design and development, FPGA Electronic Design Automation (EDA) has received extensive attention from industry and academia. Especially driven by machine learning, the running efficiency and Quality of Result (QoR) have been significantly improved. A brief overview of the conceptual connotation of FPGA EDA and machine learning is presented at first, Then, the application of machine learning to different FPGA EDA stages such as High Level Synthesis (HLS), logic synthesis, placement and routing is summarized. Finally, the development of FPGA EDA technology based on machine learning is prospected. It is expected to provide reference for experts and scholars in this field and related fields, and provide technical support for the development of China’s integrated circuit industry in the post-Moore era.
- Integrated circuit,
- Field Programmable Gate Array (FPGA),
- Machine learning,
- Electronic Design Automation (EDA)

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References(72)

References

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