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

March, 2023

2022 IEEE International Conference on Rebooting Computing (ICRC)

https://ieeexplore.ieee.org/xpl/conhome/10062291/proceeding

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Abstract

Neuromorphic computers perform computations by emulating the human brain and are expected to be indispensable for energy-efficient computing in the future. They are primarily used in spiking neural network-based machine learning applications. However, neuromorphic computers are unable to preprocess data for these applications. Currently, data is preprocessed on a CPU or a GPU-this incurs a significant cost of transferring data from the CPU/GPU to the neuromorphic processor and vice versa. This cost can be avoided if preprocessing is done on the neuromorphic processor. To efficiently preprocess data on a neuromorphic processor, we first need an efficient mechanism for encoding data that can lend itself to all general-purpose preprocessing operations. Current encoding approaches have limited applicability and may not be suitable for all preprocessing operations. In this paper, we present the virtual neuron as a mechanism for encoding integers and rational numbers on neuromorphic processors. We evaluate the performance of the virtual neuron on physical and simulated neuromorphic hardware and show that it can perform an addition operation using 23 nJ of energy on average using a mixed-signal, memristor-based neuromorphic processor. The virtual neuron encoding approach is the first step in preprocessing data on a neuromorphic processor.

Citation Information

Text

author      P. Date and S. Kulkarni and A. Young and C. D. Schuman and T. Potok and J. S. Vetter
title       Virtual Neuron: A Neuromorphic Approach for Encoding Numbers
booktitle   2022 IEEE International Conference on Rebooting Computing (ICRC)
publisher   IEEE
doi         10.1109/ICRC57508.2022.00017
url         https://ieeexplore.ieee.org/abstract/document/10062742
month       March
year        2023

Bibtex

@INPROCEEDINGS{dky:23:vnn,
    author = "P. Date and S. Kulkarni and A. Young and C. D. Schuman and T. Potok and J. S. Vetter",
    title = "Virtual Neuron: A Neuromorphic Approach for Encoding Numbers",
    booktitle = "2022 IEEE International Conference on Rebooting Computing (ICRC)",
    publisher = "IEEE",
    doi = "10.1109/ICRC57508.2022.00017",
    url = "https://ieeexplore.ieee.org/abstract/document/10062742",
    month = "March",
    year = "2023"
}