Neuromorphic computing with non-volatile memory (NVM) can significantly improve performance and lower energy consumption of machine learning tasks implemented using spike-based computations and bio-inspired learning algorithms. High voltages required to operate certain NVMs such as phase-change memory (PCM) can accelerate aging in a neuron's CMOS circuit, thereby reducing the lifetime of neuromorphic hardware. In this work, we evaluate the long-term, i.e., lifetime reliability impact of executing state-of-the-art machine learning tasks on a neuromorphic hardware, considering failure models such as negative bias temperature instability (NBTI) and time-dependent dielectric breakdown (TDDB). Based on such formulation, we show the reliability-performance trade-off obtained due to periodic relaxation of neuromorphic circuits, i.e., a stop-and-go style of neuromorphic computing.

中文翻译：

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终身可靠性感知神经形态计算的案例

具有非易失性存储器 (NVM) 的神经形态计算可以显着提高使用基于尖峰的计算和仿生学习算法实施的机器学习任务的性能并降低能耗。操作某些 NVM（例如相变存储器 (PCM)）所需的高电压会加速神经元 CMOS 电路的老化，从而缩短神经形态硬件的使用寿命。在这项工作中，我们评估了在神经形态硬件上执行最先进机器学习任务的长期可靠性影响，同时考虑了诸如负偏置温度不稳定性 (NBTI) 和瞬态电介质等故障模型故障（TDDB）。基于这样的公式，我们展示了由于神经形态电路的周期性松弛而获得的可靠性-性能权衡，即，