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首页> 外文会议>International conference on computer design >RRAMedy: Protecting ReRAM-Based Neural Network from Permanent and Soft Faults During Its Lifetime
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RRAMedy: Protecting ReRAM-Based Neural Network from Permanent and Soft Faults During Its Lifetime

机译：RRAMedy：保护基于ReRAM的神经网络在其生命周期内免受永久性和软性故障的影响

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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.
机译：新兴的忆阻器技术被认为是面向边缘的深度学习和神经形态处理器芯片的有前途的解决方案，因为它可以同时实现省电的内存计算（CiM）和常关架构。然而，由于模拟性质和不成熟的纳米级制造技术，忆阻电池遭受制造缺陷，工艺变化和老化引起的变化，这可能导致应用中的系统和功能故障。如何从永久性和软性故障中进行检测和救援，对基于边缘ReRAM的深度学习或神经形态芯片构成了重大挑战。在这项工作中，我们提出了一个边缘云协作框架RRAMedy，以实现基于忆阻器的神经加速器的原位故障检测和网络补救。在此框架中，我们提出了“对抗性示例测试”，这是一种终生的设备上故障检测技术，可以以高概率和低成本准确地检测缺陷单元和忆阻器软故障。此外，可以通过所提出的边缘云协作故障掩盖重新训练和模型更新机制以最小的边缘云通信开销来恢复模型的准确性。实验结果表明，RRAMedy可以有效地检测忆阻器的永久性和软性故障，从而保护神经加速器在其生命周期中免受准确性和性能下降的影响。

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