Journal of Robotics, Networking and Artificial Life

Volume 5, Issue 1, June 2018, Pages 71 - 74

Finding appropriate parameter voltages for driving a low-power analog silicon neuron circuit

Authors
Atsuya Tangetange@sat.t.u-tokyo.ac.jp
Department of Electrical Engineering and Information Systems, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, 153-8505, Japan
Takashi Kohnokohno@sat.t.u-tokyo.ac.jp
Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, 153-8505, Japan
Available Online 30 June 2018.
DOI
10.2991/jrnal.2018.5.1.16How to use a DOI?
Keywords
neuromorphic hardware; neuromorphic chip; silicon neurons; analog VLSI
Abstract

This research focuses on a silicon neuron circuit designed utilizing a qualitative neuronal modeling approach. In this circuit, temperature, fabrication mismatch, and secondary effects of transistors cause the difference between the intended characteristics and those in the implemented circuits. Therefore, we have to tune the bias voltages for each neuron instance to realize the desired dynamical behavior after circuit implementation. We constructed an algorithm to automatically find appropriate values for the bias voltages.

Copyright
Copyright © 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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Journal
Journal of Robotics, Networking and Artificial Life
Volume-Issue
5 - 1
Pages
71 - 74
Publication Date
2018/06/30
ISSN (Online)
2352-6386
ISSN (Print)
2405-9021
DOI
10.2991/jrnal.2018.5.1.16How to use a DOI?
Copyright
Copyright © 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Atsuya Tange
AU  - Takashi Kohno
PY  - 2018
DA  - 2018/06/30
TI  - Finding appropriate parameter voltages for driving a low-power analog silicon neuron circuit
JO  - Journal of Robotics, Networking and Artificial Life
SP  - 71
EP  - 74
VL  - 5
IS  - 1
SN  - 2352-6386
UR  - https://doi.org/10.2991/jrnal.2018.5.1.16
DO  - 10.2991/jrnal.2018.5.1.16
ID  - Tange2018
ER  -