Gangotree Chakma, Sagarvarma Sayyaparaju, Ryan Weiss and Garrett Rose

August, 2017

60th IEEE International Midwest Symposium on Circuits and Systems

http://www.mwscas2017.org

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Abstract

In this paper we present a memristive neuromorphic system for higher power and area efficiency. The system is based on a mixed signal approach considering the digital nature of the peripheral and control logics and the integration being analog. So, the system is connected digitally outside but the core is purely analog. This mixed signal approach provides the advantage of implementing neural networks with spiking events in a synchronous way. Moreover, the use of nano-sclae memristive device saves the area and power of the system and some considerations about the the device have also been proposed in the paper to make the system more energy efficient.

Citation Information

Text

author      G. Chakma and S. Sayyaparaju and R. Weiss and G. S. Rose
title       A Mixed-Signal Approach to Memristive Neuromorphic System Design
booktitle   60th IEEE International Midwest Symposium on Circuits and Systems (MWSCAS)
address     Boston, MA
month       August
year        2017
where       http://www.mwscas2017.org

Bibtex

@INPROCEEDINGS{csw:17:ams,
    author = "G. Chakma and S. Sayyaparaju and R. Weiss and G. S. Rose",
    title = "A Mixed-Signal Approach to Memristive Neuromorphic System Design",
    booktitle = "60th IEEE International Midwest Symposium on Circuits and Systems (MWSCAS)",
    address = "Boston, MA",
    month = "August",
    year = "2017",
    where = "http://www.mwscas2017.org"
}