Enhanced read resolution in reconfigurable memristive synapses for Spiking Neural Networks

• Published in: Scientific reports Vol. 14; no. 1; p. 8897
• Main Authors: Das, Hritom, Schuman, Catherine, Chakraborty, Nishith N., Rose, Garrett S.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 17.04.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166/987; 639/925/927/1007; Accuracy; Current-controlled; Design; Firing pattern; High voltage; Humanities and Social Sciences; Low power; multidisciplinary; Nervous system; Neural networks; READ current resolution; Read failure; Science; Science (multidisciplinary); Signal processing; Spiking Neural Network; Voltage; Read failure; Current-controlled; Low power; READ current resolution; Spiking Neural Network; Stochastic computing; Approximate computing
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-58947-2

The synapse is a key element circuit in any memristor-based neuromorphic computing system. A memristor is a two-terminal analog memory device. Memristive synapses suffer from various challenges including high voltage, SET or RESET failure, and READ margin issues that can degrade the distinguishability of stored weights. Enhancing READ resolution is very important to improving the reliability of memristive synapses. Usually, the READ resolution is very small for a memristive synapse with a 4-bit data precision. This work considers a step-by-step analysis to enhance the READ current resolution or the read current difference between two resistance levels for a current-controlled memristor-based synapse. An empirical model is used to characterize the HfO 2 based memristive device. 1 st and 2 nd stage device of our proposed synapse design can be scaled to enhance the READ current margin up to ∼ 4.3 × and ∼ 21%, respectively. Moreover, READ current resolution can be enhanced with run-time adaptation techniques such as READ voltage scaling and body biasing. The READ voltage scaling and body biasing can improve the READ current resolution by about 46% and 15%, respectively. TENNLab’s neuromorphic computing framework is leveraged to evaluate the effect of READ current resolution on classification, control, and reservoir computing applications. Higher READ current resolution shows better accuracy than lower resolution even when facing different levels of read noise.