Machine Learning Based Framework for Fast Resource Estimation of RTL Designs Targeting FPGAs

被引:0
|
作者
Li, Benzheng [1 ]
Zhang, Xi [1 ]
You, Hailong [1 ]
Qi, Zhongdong [1 ]
Zhang, Yuming [1 ]
机构
[1] Xidian University, School of Microelectronics, No. 2 South Taibai Road, Xi'an, Shaanxi,710071, China
关键词
714.2 Semiconductor Devices and Integrated Circuits - 721.2 Logic Elements - 723.1.1 Computer Programming Languages - 723.4 Artificial Intelligence;
D O I
暂无
中图分类号
学科分类号
摘要
33
引用
收藏
相关论文
共 50 条
  • [1] Machine Learning Based Framework for Fast Resource Estimation of RTL Designs Targeting FPGAs
    Li, Benzheng
    Zhang, Xi
    You, Hailong
    Qi, Zhongdong
    Zhang, Yuming
    ACM TRANSACTIONS ON DESIGN AUTOMATION OF ELECTRONIC SYSTEMS, 2023, 28 (02)
  • [2] FAST AND ACCURATE RESOURCE ESTIMATION OF RTL-BASED DESIGNS TARGETING FPGAS
    Schumacher, Paul
    Jha, Pradip
    2008 INTERNATIONAL CONFERENCE ON FIELD PROGRAMMABLE AND LOGIC APPLICATIONS, VOLS 1 AND 2, 2008, : 59 - 63
  • [3] A Framework for Hardware Trojan Vulnerability Estimation and Localization in RTL Designs
    Sheikh Ariful Islam
    Love Kumar Sah
    Srinivas Katkoori
    Journal of Hardware and Systems Security, 2020, 4 (3) : 246 - 262
  • [4] Machine-Learning Based Congestion Estimation for Modern FPGAs
    Maarouf, D.
    Alhyari, A.
    Abuowaimer, Z.
    Martin, T.
    Gunter, A.
    Grewal, G.
    Areibi, S.
    Vannelli, A.
    2018 28TH INTERNATIONAL CONFERENCE ON FIELD PROGRAMMABLE LOGIC AND APPLICATIONS (FPL), 2018, : 427 - 434
  • [5] FAST: An RTL Fault Simulation Framework based on RTL-to-TLM Abstraction
    Bombieri, Nicola
    Fummi, Franco
    Guarnieri, Valerio
    JOURNAL OF ELECTRONIC TESTING-THEORY AND APPLICATIONS, 2012, 28 (04): : 495 - 510
  • [6] FAST: An RTL Fault Simulation Framework based on RTL-to-TLM Abstraction
    Nicola Bombieri
    Franco Fummi
    Valerio Guarnieri
    Journal of Electronic Testing, 2012, 28 : 495 - 510
  • [7] Symbolic Regression on FPGAs for Fast Machine Learning Inference
    Tsoi, Ho Fung
    Pol, Adrian Alan
    Loncar, Vladimir
    Govorkova, Ekaterina
    Cranmer, Miles
    Dasu, Sridhara
    Elmer, Peter
    Harris, Philip
    Ojalvo, Isobel
    Pierini, Maurizio
    26TH INTERNATIONAL CONFERENCE ON COMPUTING IN HIGH ENERGY AND NUCLEAR PHYSICS, CHEP 2023, 2024, 295
  • [8] A framework for porting the NeuroBayes machine learning algorithm to FPGAs
    Baehr, S.
    Sander, O.
    Heck, M.
    Feindt, M.
    Becker, J.
    JOURNAL OF INSTRUMENTATION, 2016, 11
  • [9] Review of machine learning-based Mineral Resource estimation
    Mahoob, M. A.
    Celik, T.
    Genc, B.
    JOURNAL OF THE SOUTHERN AFRICAN INSTITUTE OF MINING AND METALLURGY, 2022, 122 (11) : 655 - 664
  • [10] Invited Paper: Verilog-to-PyG - A Framework for Graph Learning and Augmentation on RTL Designs
    Li, Yingjie
    Liu, Mingju
    Mishchenko, Alan
    Yu, Cunxi
    2023 IEEE/ACM INTERNATIONAL CONFERENCE ON COMPUTER AIDED DESIGN, ICCAD, 2023,