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Sparse Binary Matrix-Vector Multiplication on Neuromorphic Computers

Publication Type
Conference Paper
Book Title
2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)
Publication Date
Page Numbers
308 to 311
Conference Name
GrAPL 2021: Workshop on Graphs, Architectures, Programming, and Learning
Conference Location
Portland, Oregon, United States of America
Conference Sponsor
IEEE
Conference Date
-

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.