M. Rathore, R. Febbo, A. Foshie, S. N. B. Tushar, H. Das and G. S. Rose

June, 2023

GLSVLSI - Great Lakes Symposium on VLSI

https://www.glsvlsi.org/

PDF not available yet, or is only available from the conference/journal publisher.

Abstract

Neuromorphic computing systems have emerged as powerful computation tools in the field of object recognition and control systems. However, training these systems, which are usually characterized by recurrent connectivity, requires abundant computational resources: memory, computation, data, and time. Reservoir computing (RC) framework reduces this high computational training cost by focusing the training effort on only a small subset of connections thus allowing these systems to be amenable to hardware implementation. Using memristors to construct these reservoir computers reduce the area/power consumption even further. However, the inherent variability of memristors poses specific challenges. Here, we conduct an in-depth reliability analysis of challenges posed by HfO2 memristors, including cycle-to-cycle variability, read/write noise, and conductance drift in the context of RC hardware. We also explore plasticity mechanisms such as Spike-Timing Dependent Plasticity (STDP) within the scope of the spiking recurrent neural networks (SRNN) reservoir and their impact on memristor conductance drift (MCD). We present a chaotic time series prediction task applied to a Python model of the constrained hardware design achieving very low Normalized Root Mean Square Error (NRMSE) of 2 × 10-3. The analog neuron and memristive synapse circuits employed for constructing the SRNN are simulated in Cadence Spectre and the energy consumption for the Mackey-Glass (MG) time-series prediction task was found to be approximately 90 nJ.

Citation Information

Text

author      M. Rathore and R. Febbo and A. Foshie and S. N. B. Tushar and H. Das and G. S. Rose
title       Reliability Analysis of Memristive Reservoir Computing Architecture
booktitle   GLSVLSI - Great Lakes Symposium on VLSI
month       June
year        2023
pages       131-136
publisher   ACM
url         https://dl.acm.org/doi/10.1145/3583781.3590210
doi         10.1145/3583781.3590210

Bibtex

@INPROCEEDINGS{rff:23:ram,
    author = "M. Rathore and R. Febbo and A. Foshie and S. N. B. Tushar and H. Das and G. S. Rose",
    title = "Reliability Analysis of Memristive Reservoir Computing Architecture",
    booktitle = "GLSVLSI - Great Lakes Symposium on VLSI",
    month = "June",
    year = "2023",
    pages = "131-136",
    publisher = "ACM",
    url = "https://dl.acm.org/doi/10.1145/3583781.3590210",
    doi = "10.1145/3583781.3590210"
}