HAW: Hardware-Aware Point Selection for Efficient Winograd Convolution
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Signal Processing Letters"
DOI: 10.1109/lsp.2023.3258863
Abstract: Winograd's minimal filtering algorithm effectively reduces the multiplication arithmetic complexity of Convolutional Neural Networks. However, Winograd convolutions in current implementations are limited to small feature tiles for two reasons: the numerical error and the overhead… read more here.
Keywords: aware point; hardware aware; hardware; point selection ... See more keywords
Designing Efficient DNNs via Hardware-Aware Neural Architecture Search and Beyond
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems"
DOI: 10.1109/tcad.2021.3100249
Abstract: Hardware systems integrated with deep neural networks (DNNs) are deemed to pave the way for future artificial intelligence (AI). However, manually designing efficient DNNs involves nontrivial computation resources since significant trial-and-errors are required to finalize… read more here.
Keywords: hardware; hardware aware; designing efficient; efficient dnns ... See more keywords