Web: http://arxiv.org/abs/2201.10114

Jan. 26, 2022, 2:11 a.m. | Zhe Lin, Zike Yuan, Jieru Zhao, Wei Zhang, Hui Wang, Yonghong Tian

cs.LG updates on arXiv.org arxiv.org

Power estimation is the basis of many hardware optimization strategies.
However, it is still challenging to offer accurate power estimation at an early
stage such as high-level synthesis (HLS). In this paper, we propose PowerGear,
a graph-learning-assisted power estimation approach for FPGA HLS, which
features high accuracy, efficiency and transferability. PowerGear comprises two
main components: a graph construction flow and a customized graph neural
network (GNN) model. Specifically, in the graph construction flow, we introduce
buffer insertion, datapath merging, graph …

arxiv edge gnns power stage

More from arxiv.org / cs.LG updates on arXiv.org

Data Engineer, Buy with Prime

@ Amazon.com | Santa Monica, California, USA

Data Architect – Public Sector Health Data Architect, WWPS

@ Amazon.com | US, VA, Virtual Location - Virginia

[Job 8224] Data Engineer - Developer Senior

@ CI&T | Brazil

Software Engineer, Machine Learning, Planner/Behavior Prediction

@ Nuro, Inc. | Mountain View, California (HQ)

Lead Data Scientist

@ Inspectorio | Ho Chi Minh City, Ho Chi Minh City, Vietnam - Remote

Data Engineer

@ Craftable | Portugal - Remote