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Title: Energy and Area Efficiency in Neuromorphic Computing for Resource Constrained Devices

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.

Authors:
 [1];  [1]; ORCiD logo [2];  [1];  [1];  [1]
  1. University of Tennessee (UT)
  2. 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) (SC-21)
OSTI Identifier:
1454395
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: ACM Great Lakes Symposium on VLSI (GLSVLSI) - Chicago, Illinois, United States of America - 5/23/2018 4:00:00 AM-5/25/2018 4:00:00 AM
Country of Publication:
United States
Language:
English

Citation Formats

Chakma, Gangotree, Skuda, Nicholas, Schuman, Catherine D., Plank, James, Dean, Mark, and Rose, Garrett. Energy and Area Efficiency in Neuromorphic Computing for Resource Constrained Devices. United States: N. p., 2018. Web. doi:10.1145/3194554.3194611.
Chakma, Gangotree, Skuda, Nicholas, Schuman, Catherine D., Plank, James, Dean, Mark, & Rose, Garrett. Energy and Area Efficiency in Neuromorphic Computing for Resource Constrained Devices. United States. doi:10.1145/3194554.3194611.
Chakma, Gangotree, Skuda, Nicholas, Schuman, Catherine D., Plank, James, Dean, Mark, and Rose, Garrett. Tue . "Energy and Area Efficiency in Neuromorphic Computing for Resource Constrained Devices". United States. doi:10.1145/3194554.3194611. https://www.osti.gov/servlets/purl/1454395.
@article{osti_1454395,
title = {Energy and Area Efficiency in Neuromorphic Computing for Resource Constrained Devices},
author = {Chakma, Gangotree and Skuda, Nicholas and Schuman, Catherine D. and Plank, James and Dean, Mark and Rose, Garrett},
abstractNote = {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.},
doi = {10.1145/3194554.3194611},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {5}
}

Conference:
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