Web: http://arxiv.org/abs/2205.01729

May 5, 2022, 1:11 a.m. | Chih-Chyau Yang, Tian-Sheuan Chang

cs.LG updates on arXiv.org arxiv.org

With the popularity of the deep neural network (DNN), hardware accelerators
are demanded for real time execution. However, lengthy design process and fast
evolving DNN models make hardware evaluation hard to meet the time to market
need. This paper proposes a pre-RTL DNN hardware evaluator that supports
conventional layer-by-layer processing as well as the fused layer processing
for low external bandwidth requirement. The evaluator supports two
state-of-the-art accelerator architectures and finds the best hardware and
layer fusion group The experimental …

ar arxiv dnn hardware support

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

Data & Insights Strategy & Innovation General Manager

@ Chevron Services Company, a division of Chevron U.S.A Inc. | Houston, TX

Faculty members in Research areas such as Bayesian and Spatial Statistics; Data Privacy and Security; AI/ML; NLP; Image and Video Data Analysis

@ Ahmedabad University | Ahmedabad, India

Director, Applied Mathematics & Computational Research Division

@ Lawrence Berkeley National Lab | Berkeley, Ca

Business Data Analyst

@ MainStreet Family Care | Birmingham, AL

Assistant/Associate Professor of the Practice in Business Analytics

@ Georgetown University McDonough School of Business | Washington DC

Senior Data Science Writer

@ NannyML | Remote