RRAMedy: Protecting ReRAM-Based Neural Network from Permanent and Soft Faults During Its Lifetime

The emerging memristor technology is considered a promising solution to the edge-oriented deep learning and neuromorphic processor chips because it enables power-efficient Computing-in-Memory (CiM) and normally-off architecture simultaneously. However, as the analog nature and the immature nano-scale fabrication technology, the memristive cells suffer from manufacturing defects, process variations and aging-induced variations, which may incur system and function failures in applications. How to detect and rescue from the permanent and soft faults poses a significant challenge to the edge ReRAM-based deep learning or neuromorphic chips. In this work, we propose an edge-cloud collaborative framework, RRAMedy, to achieve in-situ fault detection and network remedy for memristor-based neural accelerators. In this framework, we present Adversarial Example Testing, a lifetime on-device fault detection technique, which can accurately detect defected cells and memristor soft faults with high probability and at a low cost. Furthermore, the model accuracy can be restored by the proposed edge-cloud collaborative fault-masking retraining and model updating mechanism with a minimized edge-cloud communication overhead. The experimental results show that RRAMedy can effectively detect the memristor permanent and soft faults, protecting the neural accelerator from accuracy and performance degradation in its life cycle.