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Energy and Area Efficiency in Neuromorphic Computing for Resource Constrained Devices

G. Chakma and N. D. Skuda and C. D. Schuman and J. S. Plank and M. E. Dean and G. S. Rose

May, 2018

28th ACM Great Lakes Symposium on VLSI (GLSVLSI)

https://www.glsvlsi.org/

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Abstract

Resource constrained devices are the building blocks of the internet of things (IoT) era. Since the idea behind IoT is to develop an interconnected environment where the devices are tiny enough to operate with limited resources, several control systems have been built to maintain low energy and area consumption while operating as IoT edge devices. Several researchers have begun work on implementing control systems built from resource constrained devices using machine learning. However, there are many ways such devices can achieve lower power consumption and area utilization while maximizing application efficiency. Spiky neuromorphic computing (SNC) is an emerging paradigm that can be leveraged in resource constrained devices for several emerging applications. While delivering the benefits of machine learning, SNC also helps minimize power consumption. For example, low energy memory devices (memristors) are often used to achieve low power operation and also help in reducing system area. In total, we anticipate SNC will provide computational efficiency approaching that of deep learning while using low power, resource constrained devices.

Citation Information

Text


author    G. Chakma and N. D. Skuda and C. D. Schuman and J. S. Plank and M. E. Dean and G. S. Rose
title     Energy and Area Efficiency in Neuromorphic Computing for Resource Constrained Devices
booktitle Proceedings of ACM Great Lake Symposium on VLSI (GLSVLSI)
month     May
year      2018
address   Chicago, IL

Bibtex


@INPROCEEDINGS{css:18:eae,
    author = "G. Chakma and N. D. Skuda and C. D. Schuman and J. S. Plank and M. E. Dean and G. S. Rose",
    title = "Energy and Area Efficiency in Neuromorphic Computing for Resource Constrained Devices",
    booktitle = "Proceedings of ACM Great Lake Symposium on VLSI (GLSVLSI)",
    month = "May",
    year = "2018",
    address = "Chicago, IL"
}