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Title: Sparse Binary Matrix-Vector Multiplication on Neuromorphic Computers

Abstract

Neuromorphic computers offer the opportunity for low-power, efficient computation. Though they have been primarily applied to neural network tasks, there is also the opportunity to leverage the inherent characteristics of neuromorphic computers (low power, massive parallelism, collocated processing and memory) to perform non-neural network tasks. Here, we demonstrate how an approach for performing sparse binary matrix-vector multiplication on neuromorphic computers. We describe the approach, which relies on the connection between binary matrix-vector multiplication and breadth first search, and we introduce the algorithm for performing this calculation in a neuromorphic way. We validate the approach in simulation. Finally, we provide a discussion of the runtime of this algorithm and discuss where neuromorphic computers in the future may have a computational advantage when performing this computation.

Authors:
ORCiD logo [1];  [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]
  1. ORNL
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
OSTI Identifier:
1814336
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: GrAPL 2021: Workshop on Graphs, Architectures, Programming, and Learning - Portland, Oregon, United States of America - 5/17/2021 4:00:00 AM-5/17/2021 4:00:00 AM
Country of Publication:
United States
Language:
English

Citation Formats

Schuman, Catherine, Kay, Bill, Date, Prasanna, Kannan, Ramakrishnan, Sao, Piyush, and Potok, Thomas. Sparse Binary Matrix-Vector Multiplication on Neuromorphic Computers. United States: N. p., 2021. Web.
Schuman, Catherine, Kay, Bill, Date, Prasanna, Kannan, Ramakrishnan, Sao, Piyush, & Potok, Thomas. Sparse Binary Matrix-Vector Multiplication on Neuromorphic Computers. United States.
Schuman, Catherine, Kay, Bill, Date, Prasanna, Kannan, Ramakrishnan, Sao, Piyush, and Potok, Thomas. 2021. "Sparse Binary Matrix-Vector Multiplication on Neuromorphic Computers". United States. https://www.osti.gov/servlets/purl/1814336.
@article{osti_1814336,
title = {Sparse Binary Matrix-Vector Multiplication on Neuromorphic Computers},
author = {Schuman, Catherine and Kay, Bill and Date, Prasanna and Kannan, Ramakrishnan and Sao, Piyush and Potok, Thomas},
abstractNote = {Neuromorphic computers offer the opportunity for low-power, efficient computation. Though they have been primarily applied to neural network tasks, there is also the opportunity to leverage the inherent characteristics of neuromorphic computers (low power, massive parallelism, collocated processing and memory) to perform non-neural network tasks. Here, we demonstrate how an approach for performing sparse binary matrix-vector multiplication on neuromorphic computers. We describe the approach, which relies on the connection between binary matrix-vector multiplication and breadth first search, and we introduce the algorithm for performing this calculation in a neuromorphic way. We validate the approach in simulation. Finally, we provide a discussion of the runtime of this algorithm and discuss where neuromorphic computers in the future may have a computational advantage when performing this computation.},
doi = {},
url = {https://www.osti.gov/biblio/1814336}, journal = {},
number = ,
volume = ,
place = {United States},
year = {2021},
month = {6}
}

Conference:
Other availability
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