RFAM: RESET-Failure-Aware-Model for HfO2-based Memristor to Enhance the Reliability of Neuromorphic Design

Das, Hritom; Rathore, Manu; Febbo, Rocco; Liehr, Maximilian; Cady, Nathaniel C.; Rose, Garrett S.

doi:10.1145/3583781.3590211

Cited by 8 publications

References 12 publications

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Enhanced read resolution in reconfigurable memristive synapses for Spiking Neural Networks

Das,

Schuman,

Chakraborty

et al. 2024

Sci Rep

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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 $${\hbox {HfO}}_{2}$$ HfO 2 based memristive device. $$1\textrm{st}$$ 1 st and $$2\textrm{nd}$$ 2 nd stage device of our proposed synapse design can be scaled to enhance the READ current margin up to $$\sim$$ ∼ 4.3$$\times$$ × and $$\sim$$ ∼ 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.

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Enhanced read resolution in reconfigurable memristive synapses for Spiking Neural Networks

Das,

Schuman,

Chakraborty

et al. 2024

Sci Rep

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HfO₂-Based Synaptic Spiking Neural Network Evaluation to Optimize Design and Testing Cost

Tushar,

Das,

Rose

2024

2024 IEEE International Symposium on Circuits and Systems (ISCAS)

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In-Sensor Motion Recognition with Memristive System and Light Sensing Surfaces

Das,

Fahad,

Tushar

et al. 2024

2024 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)

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RFAM: RESET-Failure-Aware-Model for HfO2-based Memristor to Enhance the Reliability of Neuromorphic Design

Cited by 8 publications

References 12 publications

Enhanced read resolution in reconfigurable memristive synapses for Spiking Neural Networks

Enhanced read resolution in reconfigurable memristive synapses for Spiking Neural Networks

HfO₂-Based Synaptic Spiking Neural Network Evaluation to Optimize Design and Testing Cost

In-Sensor Motion Recognition with Memristive System and Light Sensing Surfaces

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RFAM: RESET-Failure-Aware-Model for HfO2-based Memristor to Enhance the Reliability of Neuromorphic Design

Cited by 8 publications

References 12 publications

Enhanced read resolution in reconfigurable memristive synapses for Spiking Neural Networks

Enhanced read resolution in reconfigurable memristive synapses for Spiking Neural Networks

HfO2-Based Synaptic Spiking Neural Network Evaluation to Optimize Design and Testing Cost

In-Sensor Motion Recognition with Memristive System and Light Sensing Surfaces

Contact Info

Product

Resources

About

HfO₂-Based Synaptic Spiking Neural Network Evaluation to Optimize Design and Testing Cost