Neuromorphic computing based on spiking neural network (SNN) shows good energy-efficiency. However, it is inefficient for SNN to perform the convolution based on frame. It may contain a lot of redundant information in the frame. The output of Dynamic Vision Sensors (DVS) is a stream event based on Address Event Representation (AER). The asynchronous nature of AER events makes the event-based convolution reflect the characteristics of SNN low energy consumption. This article presents an SNN hardware inference engine based on an asynchronous Processing Element (PE) array with AER events as input. The engine uses a convolution algorithm based on AER events. This design also uses distributed storage in the PE array to store the state of neurons to reduce the cost of memory access. The experimental results show that the design can achieve a recognition accuracy of 98.0% for the MNIST AER dataset. The design can perform the reference process more efficiently in the case where the accuracy of the loss is negligible. During the filling and draining processes of the systolic array, the number of active PE units in our PE array is reduced and, thus, the average power consumption per PE unit is drastically decreased.

中文翻译：

---

亚西

基于脉冲神经网络（SNN）的神经形态计算显示出良好的能量效率。然而，SNN 进行基于帧的卷积是低效的。它可能在帧中包含大量冗余信息。动态视觉传感器 (DVS) 的输出是基于地址事件表示 (AER) 的流事件。AER事件的异步特性使得基于事件的卷积体现了SNN低能耗的特点。本文介绍了一个基于异步处理元素 (PE) 阵列的 SNN 硬件推理引擎，其中 AER 事件作为输入。该引擎使用基于 AER 事件的卷积算法。该设计还使用PE阵列中的分布式存储来存储神经元的状态，以降低内存访问的成本。实验结果表明，该设计对 MNIST AER 数据集的识别准确率可以达到 98.0%。在损失的准确性可以忽略不计的情况下，该设计可以更有效地执行参考过程。在收缩阵列的填充和排出过程中，我们的 PE 阵列中的活动 PE 单元的数量减少了，因此每个 PE 单元的平均功耗大大降低。