论文标题
终身可靠性感知神经形态计算的情况
A Case for Lifetime Reliability-Aware Neuromorphic Computing
论文作者
论文摘要
具有非易失性记忆（NVM）的神经形态计算可以显着提高性能，并降低使用基于Spike的计算和生物启发的学习算法实施的机器学习任务的能源消耗。操作某些NVM所需的高电压，例如相变内存（PCM）可以在神经元的CMOS电路中加速衰老，从而降低神经形态硬件的寿命。在这项工作中，我们评估了执行最新机器学习任务对神经形态硬件的长期，即终身可靠性的影响，考虑到故障模型，例如负偏置温度不稳定性（NBTI）和时间依赖时间依赖性的介质分解（TDDB）。基于这种表述，我们显示了由于神经形态电路的定期松弛而获得的可靠性 - 性能权衡，即神经形态计算的停止和go风格。
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.
