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arXiv · 2412.03089

Online Soft Error Tolerance in ReRAM Crossbars for Deep Learning Accelerators

Benyamin Khezeli·Hamid Reza Zarandi·Elham Cheshmikhani
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Abstract

Resistive Random-Access Memory (ReRAM) crossbar arrays are promising candidates for in-situ matrix-vector multiplication (MVM), a frequent operation in Deep Learning algorithms. Despite their advantages, these emerging non-volatile memories are susceptible to errors due to non-idealities such as immature fabrication processes and runtime errors, which lead to accuracy degradation in Processing-in-Memory (PIM) accelerators. This paper proposes an online soft error detection and correction method in ReRAM crossbar arrays. We utilize a test input vector and Error Correcting Codes (ECCs) to detect and correct faulty columns. The proposed approach demonstrates near fault-free accuracy for Neural Networks (NNs) on MNIST and CIFAR-10 datasets, with low area overhead and power consumption compared to recent methods.

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Benyamin Khezeli, Hamid Reza Zarandi, Elham Cheshmikhani. 2024-12-04. Online Soft Error Tolerance in ReRAM Crossbars for Deep Learning Accelerators. https://arxiv.org/abs/2412.03089

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