Prasanna Date, Shruti Kulkarni, Aaron Young, Catherine Schuman, Thomas Potok, and Jeffrey Vetter

July, 2023

Nature Scientific Reports

https://www.nature.com/articles/s41598-023-35005-x

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Abstract

Neuromorphic computers emulate the human brain while being extremely power efficient for computing tasks. In fact, they are poised to be critical for energy-efficient computing in the future. Neuromorphic computers are primarily used in spiking neural network–based machine learning applications. However, they are known to be Turing-complete, and in theory can perform all general-purpose computation. One of the biggest bottlenecks in realizing general-purpose computations on neuromorphic computers today is the inability to efficiently encode data on the neuromorphic computers. To fully realize the potential of neuromorphic computers for energy-efficient general-purpose computing, efficient mechanisms must be devised for encoding numbers. Current encoding mechanisms (e.g., binning, rate-based encoding, and time-based encoding) have limited applicability and are not suited for general-purpose computation. In this paper, we present the virtual neuron abstraction as a mechanism for encoding and adding integers and rational numbers by using spiking neural network primitives. We evaluate the performance of the virtual neuron on physical and simulated neuromorphic hardware. We estimate that the virtual neuron could perform an addition operation using just 23 nJ of energy on average with a mixed-signal, memristor-based neuromorphic processor. We also demonstrate the utility of the virtual neuron by using it in some of the μ-recursive functions, which are the building blocks of general-purpose computation.

Citation Information

Text

author      P. Date and S. Kulkarni and A. Young and C.D. Schuman and T. Potok and J. Vetter
title       Encoding integers and rationals on neuromorphic computers using virtual neuron
journal     Nature Scientific Reports
volume      13
number      10975
year        2023
url         https://www.nature.com/articles/s41598-023-35005-x
doi         10.1038/s41598-023-35005-x

Bibtex

@ARTICLE{dky:23:eir,
    author = "P. Date and S. Kulkarni and A. Young and C.D. Schuman and T. Potok and J. Vetter",
    title = "Encoding integers and rationals on neuromorphic computers using virtual neuron",
    journal = "Nature Scientific Reports",
    volume = "13",
    number = "10975",
    year = "2023",
    url = "https://www.nature.com/articles/s41598-023-35005-x",
    doi = "10.1038/s41598-023-35005-x"
}