Gangotree Chakma, Md Musabbir Adnan, Austin R. Wyer, Ryan Weiss, Catherine D. Schuman and Garrett S. Rose

March, 2018

IEEE Journal on Emerging and Selected Topics in Circuits and Systems

http://ieeexplore.ieee.org/document/8119503/?section=abstract

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Abstract

Neuromorphic computing is a non-von Neumann computer architecture for the post Moore’s law era of computing. Since a main focus of the post Moore’s law era is energy-efficient computing with fewer resources and less area, neuromorphic computing contributes effectively in this research. In this paper we present a memristive neuromorphic system for improved power and area efficiency. Our particular mixed-signal approach implements neural networks with spiking events in a synchronous way. Moreover, the use of nano-scale memristive devices saves both area and power in the system. We also provide device-level considerations that make the system more energy-efficient. The proposed system additionally includes synchronous digital long term plasticity (DLTP), an online learning methodology that helps the system train the neural networks during the operation phase and improves the efficiency in learning considering the power consumption and area overhead.

Citation Information

Text

author       G. Chakma and M. M. Adnan and A. R. Wyer and R. Weiss and C. D. Schuman and G. S. Rose
title        Memristive Mixed-Signal Neuromorphic Systems: Energy-Efficient Learning at the Circuit-Level
journal      IEEE Journal on Emerging and Selected topics in Circuits and Systems (JETCAS)
volume       8
number       1
month        March
year         2018
pages        125-136
keywords     Biological neural networks;Memristors;Neuromorphics;Neurons;Resistance;Switches;Memristor;emerging technology;neuromorphic computing
doi          10.1109/JETCAS.2017.2777181
ISSN         2156-3357

Bibtex

@ARTICLE{caw:18:mms,
   author = "G. Chakma and M. M. Adnan and A. R. Wyer and R. Weiss and C. D. Schuman and G. S. Rose",
   title = "Memristive Mixed-Signal Neuromorphic Systems: Energy-Efficient Learning at the Circuit-Level",
   journal = "IEEE Journal on Emerging and Selected topics in Circuits and Systems (JETCAS)",
   volume = "8",
   number = "1",
   month = "March",
   year = "2018",
   pages = "125-136",
   keywords = "Biological neural networks;Memristors;Neuromorphics;Neurons;Resistance;Switches;Memristor;emerging technology;neuromorphic computing",
   doi = "10.1109/JETCAS.2017.2777181",
   ISSN = "2156-3357"
}