Hritom Das, Rocco D. Febbo, Charles P. Rizzo, Nishith N. Chakraborty, James S. Plank, Garrett S. Rose

September, 2023

IEEE Journal on Emerging and Selected Topics in Circuits and Systems

https://ieeexplore.ieee.org/document/10239501

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Abstract

The synapse is a key element of neuromorphic computing in terms of efficiency and accuracy. In this paper, an optimized current-controlled memristive synapse circuit is proposed. Our proposed synapse demonstrates reliability in the face of process variation and the inherent stochastic behavior of memristors. Up to an 82% energy optimization can be seen during the SET operation over prior work. In addition, the READ process shows up to 54% energy savings. Our current-controlled approach also provides more reliable programming over traditional programming methods. This design is demonstrated with a 4-bit memory precision configuration. Using a spiking neural network (SNN), a neuromorphic application analysis was performed with this precision configuration. Our optimized design showed up to a 82% improvement in control applications and a 2.7x improvement in classification applications compared with other design cases.

Citation Information

Text

author     H. Das and R. D. Febbo and C. P. Rizzo and N. N. Chakraborty
           and J. S. Plank and G. S. Rose
title      Optimizations for a Current-Controlled Memristor-based Neuromorphic Synapse Design
journal    IEEE Journal on Emerging and Selected Topics in Circuits and Systems
doi        10.1109/JETCAS.2023.3312163
year       2023

Bibtex

@ARTICLE{dfr:23:ofc,
    author = "H. Das and R. D. Febbo and C. P. Rizzo and N. N. Chakraborty
               and J. S. Plank and G. S. Rose",
    title = "Optimizations for a Current-Controlled Memristor-based Neuromorphic Synapse Design",
    journal = "IEEE Journal on Emerging and Selected Topics in Circuits and Systems",
    doi = "10.1109/JETCAS.2023.3312163",
    year = "2023"
}