%0 Conference Proceedings
%T Multiple Algorithms Against Multiple Hardware Architectures: Data-Driven Exploration on Deep Convolution Neural Network
%+ Beihang University (BUAA)
%+ Science and Technology on Special System Simulation
%A Xu, Chongyang
%A Luan, Zhongzhi
%A Gao, Lan
%A Wang, Rui
%A Zhang, Han
%A Zhang, Lianyi
%A Liu, Yi
%A Qian, Depei
%Z Part 8: Short Papers
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 16th IFIP International Conference on Network and Parallel Computing (NPC)
%C Hohhot, China
%Y Xiaoxin Tang
%Y Quan Chen
%Y Pradip Bose
%Y Weiming Zheng
%Y Jean-Luc Gaudiot
%I Springer International Publishing
%3 Network and Parallel Computing
%V LNCS-11783
%P 371-375
%8 2019-08-23
%D 2019
%R 10.1007/978-3-030-30709-7_36
%K Convolutional neural network
%K Hardware architecture
%K Performance evaluation
%Z Computer Science [cs]Conference papers
%X With the rapid development of deep learning (DL), various convolution neural network (CNN) models have been developed. Moreover, to execute different DL workloads efficiently, many accelerators have been proposed. To guide the design of both CNN models and hardware architectures for a high-performance inference system, we choose five types of CNN models and test them on six processors and measure three metrics. With our experiments, we get two observations and conduct two insights for the design of CNN algorithms and hardware architectures.
%G English
%Z TC 10
%Z WG 10.3
%2 https://inria.hal.science/hal-03770535/document
%2 https://inria.hal.science/hal-03770535/file/486810_1_En_36_Chapter.pdf
%L hal-03770535
%U https://inria.hal.science/hal-03770535
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC10
%~ IFIP-NPC
%~ IFIP-WG10-3
%~ IFIP-LNCS-11783