N. N. Chakraborty and H. Das and G. S. Rose

August, 2023

66th IEEE International Midwest Symposium on Circuits and Systems (MWSCAS)

https://ieeexplore.ieee.org/document/10406066

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Abstract

In this paper, in an effort to implement an unsupervised learning algorithm for silicon neurons, we present a mixed-signal Leaky Integrate-And-Fire (LIF) neuron with two different integrated homeostasis circuits using programmable leak. The homeostasis mechanism is realized by controlling the charge accumulation rate on the neuron integrator by varying the leakage rate using external signals. The proposed homeostasis circuits have been simulated using a 65nm CMOS process and their performances have been compared with existing homeostasis implementations. Results show that our designs achieve 12.8%-18.1% power improvements and 25.1%-48.2% area improvements over similar prior implementation. Also, power consumption can be reduced in the circuits by adjusting the leakage through bias currents.

Citation Information

Text

author      N. N. Chakraborty and H. Das and G. S. Rose
title       Homeostatic Plasticity in a Leaky Integrate and Fire Neuron using Tunable Leak
booktitle   66th IEEE International Midwest Symposium on Circuits and Systems (MWSCAS)
address     Tempe, AZ
month       August
year        2023
doi         10.1109/MWSCAS57524.2023.10406066
where       https://ieeexplore.ieee.org/document/10406066

Bibtex

@INPROCEEDINGS{cdr:23:hpl,
    author = "N. N. Chakraborty and H. Das and G. S. Rose",
    title = "Homeostatic Plasticity in a Leaky Integrate and Fire Neuron using Tunable Leak",
    booktitle = "66th IEEE International Midwest Symposium on Circuits and Systems (MWSCAS)",
    address = "Tempe, AZ",
    month = "August",
    year = "2023",
    doi = "10.1109/MWSCAS57524.2023.10406066",
    where = "https://ieeexplore.ieee.org/document/10406066"
}