Learning-Enabled NoC Design for Heterogeneous Manycore Systems

被引：0

作者：

Kim, Ryan Gary ^{[1
]}

机构：

[1] Colorado State Univ, Dept Elect & Comp Engn, Ft Collins, CO 80524 USA

来源：

PROCEEDINGS OF THE TWENTYFIRST INTERNATIONAL SYMPOSIUM ON QUALITY ELECTRONIC DESIGN (ISQED 2020) | 2020年

关键词：

NoC; Wireless NoC; 3D NoC; Multi-objective optimization; Machine learning; CPU-GPU systems; SPACE EXPLORATION; OPTIMIZATION;

D O I：

10.1109/isqed48828.2020.9137000

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

As systems grow in specialization (e.g., domain specific architectures), we need the tools to handle the growing design space from increased heterogeneity and system sizes. In this paper, we investigate the specific challenges posed by heterogeneous systems on the NoC in two separate contexts: wireless- and 3D-enabled, formulate each as a separate multi-objective optimization problem, and present a machine learning based design space exploration technique, MOO-STAGE, to intelligently explore this growing design space.

引用

页码：268 / 272

页数：5

共 50 条

[31] Machine learning-enabled discovery and design of membrane-active peptides
Lee, Ernest Y.
Wong, Gerard C. L.
Ferguson, Andrew L.
[J]. BIOORGANIC & MEDICINAL CHEMISTRY, 2018, 26 (10) : 2708 - 2718
[32] Machine learning-enabled retrobiosynthesis of molecules
Yu, Tianhao
Boob, Aashutosh Girish
Volk, Michael J.
Liu, Xuan
Cui, Haiyang
Zhao, Huimin
[J]. NATURE CATALYSIS, 2023, 6 (2) : 137 - 151
[33] Hardware Microkernels for Heterogeneous Manycore Systems
Agron, Jason
Andrews, David
[J]. 2009 INTERNATIONAL CONFERENCE ON PARALLEL PROCESSING WORKSHOPS (ICPPW 2009), 2009, : 19 - 26
[34] Formal verification of robustness and resilience of learning-enabled state estimation systems
Huang, Wei
Zhou, Yifan
Jin, Gaojie
Sun, Youcheng
Meng, Jie
Zhang, Fan
Huang, Xiaowei
[J]. NEUROCOMPUTING, 2024, 585
[35] MoDALAS: addressing assurance for learning-enabled autonomous systems in the face of uncertainty
Michael Austin Langford
Kenneth H. Chan
Jonathon Emil Fleck
Philip K. McKinley
Betty H. C. Cheng
[J]. Software and Systems Modeling, 2023, 22 : 1543 - 1563
[36] Learning-Enabled Residential Demand Response
Mieth, Robert
Acharya, Samrat
Hassan, Ali
Dvorkin, Yury
[J]. IEEE ELECTRIFICATION MAGAZINE, 2021, 9 (01): : 36 - 44
[37] Machine Learning-Enabled Tactile Sensor Design for Dynamic Touch Decoding
Lu, Yuyao
Kong, Depeng
Yang, Geng
Wang, Ruohan
Pang, Gaoyang
Luo, Huayu
Yang, Huayong
Xu, Kaichen
[J]. ADVANCED SCIENCE, 2023, 10 (32)
[38] Certifying Learning-Enabled Autonomous Cyber Physical Systems - A Deployment Perspective
Singh, Suraj
Hazra, Somnath
Dey, Sumanta
Dey, Soumyajit
[J]. PROCEEDINGS OF THE 37TH INTERNATIONAL CONFERENCE ON VLSI DESIGN, VLSID 2024 AND 23RD INTERNATIONAL CONFERENCE ON EMBEDDED SYSTEMS, ES 2024, 2024, : 270 - 275
[39] Heterogeneous Manycore Architectures Enabled by Processing-in-Memory for Deep Learning: From CNNs to GNNs
Joardar, Biresh Kumar
Arka, Aqeeb Iqbal
Doppa, Janardhan Rao
Pande, Partha Pratim
Li, Hai
Chakrabarty, Krishnendu
[J]. 2021 IEEE/ACM INTERNATIONAL CONFERENCE ON COMPUTER AIDED DESIGN (ICCAD), 2021,
[40] MoDALAS: Model-Driven Assurance for Learning-Enabled Autonomous Systems
Langford, Michael Austin
Chan, Kenneth H.
Fleck, Jonathon Emil
McKinley, Philip K.
Cheng, Betty H. C.
[J]. 24TH INTERNATIONAL CONFERENCE ON MODEL-DRIVEN ENGINEERING LANGUAGES AND SYSTEMS (MODELS 2021), 2021, : 182 - 193

← 1 2 3 4 5 →