Learning the sparsity for ReRAM
Author:
Affiliation:
1. UCSB and Tsinghua University, Beijing, China
2. Tsinghua University, Beijing, China
3. UCSB
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3287624.3287715
Reference21 articles.
1. Metal–Oxide RRAM
2. PRIME: A Novel Processing-in-Memory Architecture for Neural Network Computation in ReRAM-Based Main Memory
3. ISAAC: A Convolutional Neural Network Accelerator with In-Situ Analog Arithmetic in Crossbars
4. PipeLayer: A pipelined reram-based accelerator for deep learning;Song L.;HPCA,2017
5. Learning both weights and connections for efficient neural network;Han S.;NIPS,2015
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