Communication-Computation Efficient Device-Edge Co-Inference via AutoML

Communication-Computation Efficient Device-Edge Co-Inference via AutoML

30 August 2021

Jiawei Shao

Papers citing "Communication-Computation Efficient Device-Edge Co-Inference via AutoML"

7 / 7 papers shown

Title
BottleNet++: An End-to-End Approach for Feature Compression in Device-Edge Co-Inference Systems Jiawei Shao Jun Zhang 41 160 0 31 Oct 2019
AMC: AutoML for Model Compression and Acceleration on Mobile Devices Yihui He Ji Lin Zhijian Liu Hanrui Wang Li Li Song Han 65 1,346 0 10 Feb 2018
A Brief Survey of Deep Reinforcement Learning Kai Arulkumaran M. Deisenroth Miles Brundage Anil Anthony Bharath OffRL 96 2,792 0 19 Aug 2017
Efficient Processing of Deep Neural Networks: A Tutorial and Survey Vivienne Sze Yu-hsin Chen Tien-Ju Yang J. Emer AAML 3DV 94 3,002 0 27 Mar 2017
Pruning Filters for Efficient ConvNets Hao Li Asim Kadav Igor Durdanovic H. Samet H. Graf 3DPC 160 3,676 0 31 Aug 2016
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding Song Han Huizi Mao W. Dally 3DGS 192 8,793 0 01 Oct 2015
Continuous control with deep reinforcement learning Timothy Lillicrap Jonathan J. Hunt Alexander Pritzel N. Heess Tom Erez Yuval Tassa David Silver Daan Wierstra 181 13,174 0 09 Sep 2015