An Empirical Fault Vulnerability Exploration of ReRAM-based Process-in-Memory CNN Accelerators
Resistive random-access memory (ReRAM)-based Processing-in-Memory (PIM) accelerator is a promising platform for processing massively memory intensive matrix-vector multiplications of neural networks in parallel domain, due to its capability of analog computation, ultra-high density, near-zero leakage current, and non-volatility. Despite many advantages, ReRAM-based accelerators are highly error-prone due to limitations of technology fabrication that lead to process variations and defects. These limitations degrade the accuracy of Deep Convolutional Neural Networks (Deep CNNs) running on PIM accelerators. While these CNNs accelerators are widely deployed in safety-critical systems, their v...
| Authors | Aniseh Dorostkar, Hamed Farbeh, Hamid R. Zarandi |
|---|---|
| Primary category | cs.AR |
| Published | 4 October 2026 |
| Last updated at source | 4 October 2026 |
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