Energy-Efficient ReRAM-Based ML Training via Mixed Pruning and Reconfigurable ADC
Author:
Affiliation:
1. School of EECS Washington State University,Pullman,WA,USA
2. University of Houston,Houston,TX,USA
3. Arizona State University,Tempe,AZ,USA
Funder
US National Science Foundation (NSF)
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10244176/10244185/10244258.pdf?arnumber=10244258
Reference19 articles.
1. The lottery ticket hypothesis: Finding sparse, trainable neural networks;frankle;ICLRE,2019
2. SAMBA: Sparsity Aware In-Memory Computing Based Machine Learning Accelerator
3. Dropout: A simple way to prevent neural networks from overfitting;srivastava;JMLR,2014
4. One ticket to win them all: Generalizing lottery ticket initializations across datasets and optimizers;morcos;NeurIPS,2019
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