Impact of Operating Temperature on Pattern Recognition Accuracy of Resistive Array-Based Hardware Neural Networks

In hardware neural networks (HNNs), different operating temperatures cause variation in conductance of resistive arrays, and they can significantly distort the information of the synaptic weights, leading to a considerable loss in pattern recognition accuracy. In this study, a WO
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-based resistive device is characterized with varying ambient temperatures, and 1k-bit synapse arrays are evaluated. A systematic analysis of the impact of operating temperature on the array-based HNNs is executed using neural network simulations. Moreover, we propose a temperature compensator (TC) that can mitigate anomalous array behavior without modifying the readout circuitry. Our results have demonstrated successful accuracy recovery of the array-based HNN over a wide range of operating temperatures.