E-UPQ: Energy-Aware Unified Pruning-Quantization Framework for CIM Architecture
Author:
Affiliation:
1. Graduate Institute of Electronics Engineering, National Taiwan University, Taipei, Taiwan
2. Department of Electrical Engineering and the Graduate Institute of Electronics Engineering, National Taiwan University, Taipei, Taiwan
Funder
Ministry of Science and Technology of Taiwan
NOVATEK Fellowship
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/5503868/10078357/10038513.pdf?arnumber=10038513
Reference31 articles.
1. Mixed Precision Quantization for ReRAM-based DNN Inference Accelerators
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5. DoReFa-Net: Training low bitwidth convolutional neural networks with low bitwidth gradients;zhou;arXiv 1606 06160 [cs],2016
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