Energy efficiency and delay time in the Internet of Things (IoT) system are becoming increasingly significant, especially for the emerging memristor-based crossbar arrays for smart edge computing. This article aims to find a solution for increasing energy efficiency and reducing the delay time, thereby improving the performance of ANNs in edge computing systems. The Number of Pulses Compression (NPC) method is proposed to optimize pulse distribution, energy consumption, and latency by compressing the number of pulses in every weight update step. The NPC method is implemented and verified in a memristor-based hardware simulator based on the MNIST and CIFAR-10 dataset under different circumstances of variations, failure rates, aging effects, architectures, and algorithms. The experimental results show that the NPC method can not only alleviate the uneven distribution of writing pulses but also save the writing energy of the crossbar array by 7.7--26.9 percent and reduce the writing latency by 30.0--50.0 percent. Additionally, the timing regularity of the system is enhanced by the NPC method.

中文翻译：

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改善基于忆阻器的人工神经网络在边缘计算中的性能

物联网 (IoT) 系统中的能源效率和延迟时间变得越来越重要，尤其是对于用于智能边缘计算的新兴基于忆阻器的交叉阵列。本文旨在找到一种提高能源效率和减少延迟时间的解决方案，从而提高边缘计算系统中 ANN 的性能。提出了脉冲数压缩 (NPC) 方法，通过压缩每个权重更新步骤中的脉冲数来优化脉冲分布、能量消耗和延迟。NPC 方法在基于 MNIST 和 CIFAR-10 数据集的基于忆阻器的硬件模拟器中在变化、故障率、老化效应、架构和算法的不同情况下实现和验证。实验结果表明，NPC方法不仅可以缓解写入脉冲的不均匀分布，而且可以使交叉阵列的写入能量节省7.7--26.9%，写入延迟降低30.0--50.0%。此外，NPC方法增强了系统的时序规律性。