Dynamic Approximation with Feedback Control for Energy-Efficient Recurrent Neural Network Hardware-Reference-Cited by-同舟云学术

Dynamic Approximation with Feedback Control for Energy-Efficient Recurrent Neural Network Hardware

Published:2016-08-08 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2016 International Symposium on Low Power Electronics and Design
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Author:

Kung Jaeha¹,Kim Duckhwan¹,Mukhopadhyay Saibal¹

Affiliation:

1. Georgia Institute of Technology, Atlanta, GA, USA

Funder

Office of Naval Research

National Science Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/2934583.2934626

Reference21 articles.

1. C. Szegedy etal "Going deeper with convolutions " in CoRR abs/1409.4842 2014. C. Szegedy et al. "Going deeper with convolutions " in CoRR abs/1409.4842 2014.

2. Optimizing FPGA-based Accelerator Design for Deep Convolutional Neural Networks

3. Z. C. Lipton etal "Learning to diagnose with LSTM recurrent neural network " in CoRR abs/1511.03677 2015. Z. C. Lipton et al. "Learning to diagnose with LSTM recurrent neural network " in CoRR abs/1511.03677 2015.

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