A. Kumar, J. Park, Y. Zhou, J. Kim, S. Jain, C. D. Schuman, G. Cauwenberghws and D. Kuzum

April, 2024

Neuro Inspired Computational Elements (NICE)

https://ieeexplore.ieee.org/document/10549689

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Abstract

In this work, we present a hardware implementation of spiking neural network (SNN) model using trilayer bulk RRAM crossbar arrays for an autonomous navigation/racing task. We demonstrate multi-level switching in MΩ regime using trilayer (Al2O3/TiO2/TiOx) bulk RRAM devices without needing a compliance current. We also present a neuromorphic compute-in-memory (CIM) architecture based on trilayer bulk RRAM crossbars using energy-efficient voltage sensing and row differential encoding of weights to experimentally implement highly accurate matrix-vector-multiplications (MVM). Our results suggest that use of our bulk RRAM can reduce the energy consumption by more than 100× compared to other filamentary RRAMs. This work paves the way for neuromorphic computing at the edge under strict area and energy constraints.

Citation Information

Text

author     A. Kumar and J. Park and Y. Zhou and J. Kim and S. Jain and C. D. Schuman and G. Cauwenberghws and D. Kuzum
title      Energy Efficient Implementation of {MVM} Operations Using Filament-Free Bulk {RRAM} Array
booktitle  Neuro Inspired Computational Elements (NICE)
address    La Jolla, CA
month      April
year       2024
doi        10.1109/NICE61972.2024.10549369
url        https://ieeexplore.ieee.org/abstract/document/10549369

Bibtex

@INPROCEEDINGS{kpz:24:eei,
    author = "A. Kumar and J. Park and Y. Zhou and J. Kim and S. Jain and C. D. Schuman and G. Cauwenberghws and D. Kuzum",
    title = "Energy Efficient Implementation of {MVM} Operations Using Filament-Free Bulk {RRAM} Array",
    booktitle = "Neuro Inspired Computational Elements (NICE)",
    address = "La Jolla, CA",
    month = "April",
    year = "2024",
    doi = "10.1109/NICE61972.2024.10549369",
    url = "https://ieeexplore.ieee.org/abstract/document/10549369"
}