SAMBA: Sparsity Aware In-Memory Computing Based Machine Learning Accelerator

Author:

Kim Dong Eun1ORCID,Ankit Aayush2ORCID,Wang Cheng3ORCID,Roy Kaushik1ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA

2. Microsoft, San Jose, CA, USA

3. Department of Electrical and Computer Engineering, Iowa State University, Ames, IA, USA

Funder

Center for Brain Inspired Computing

Semiconductor Research Corporation

Defense Advanced Research Projects Agency

National Science Foundation

IARPA MicroE4AI program

DoD Vannevar Bush Fellowship

Army Research Laboratory

U.S. Department of Energy

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Hardware and Architecture,Theoretical Computer Science,Software

Reference36 articles.

1. To prune, or not to prune: Exploring the efficacy of pruning for model compression;zhu,2017

2. Learning the sparsity for ReRAM

3. Deep learning inference in facebook data centers: Characterization, performance optimizations and hardware implications;park,2018

4. SNrram: An Efficient Sparse Neural Network Computation Architecture Based on Resistive Random-Access Memory

5. A Massively Parallel Coprocessor for Convolutional Neural Networks

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