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 27 160 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 60 1,346 0 10 Feb 2018
A Brief Survey of Deep Reinforcement Learning Kai Arulkumaran M. Deisenroth Miles Brundage Anil Anthony Bharath OffRL 81 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 86 3,002 0 27 Mar 2017
Pruning Filters for Efficient ConvNets Hao Li Asim Kadav Igor Durdanovic H. Samet H. Graf 3DPC 151 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 170 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 141 13,174 0 09 Sep 2015

We use cookies and other tracking technologies to improve your browsing experience on our website, to show you personalized content and targeted ads, to analyze our website traffic, and to understand where our visitors are coming from. See our policy.