Synchronous Unsupervised STDP Learning with Stochastic STT-MRAM Switching

• URL: http://arxiv.org/abs/2112.05707v1
• Date: Fri, 10 Dec 2021 17:59:46 GMT
• Title: Synchronous Unsupervised STDP Learning with Stochastic STT-MRAM Switching
• Authors: Peng Zhou, Julie A. Smith, Laura Deremo, Stephen K. Heinrich-Barna, Joseph S. Friedman
• Abstract summary: The use of resistance states for storing weights in neuromorphic systems is impeded by fabrication imprecision and analogity. This paper proposes a synchronous spiking network with clocked circuits that perform unsupervised learning system. The proposed system enables a single-layer network to achieve 90% accuracy on the MNIST dataset.
• Score: 3.2894781846488494
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The use of analog resistance states for storing weights in neuromorphic systems is impeded by fabrication imprecision and device stochasticity that limit the precision of synapse weights. This challenge can be resolved by emulating analog behavior with the stochastic switching of the binary states of spin-transfer torque magnetoresistive random-access memory (STT-MRAM). However, previous approaches based on STT-MRAM operate in an asynchronous manner that is difficult to implement experimentally. This paper proposes a synchronous spiking neural network system with clocked circuits that perform unsupervised learning leveraging the stochastic switching of STT-MRAM. The proposed system enables a single-layer network to achieve 90% inference accuracy on the MNIST dataset.

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