Dynamic vision sensor (DVS) is novel neuromorphic imaging device that generates asynchronous events. Despite the high temporal resolution and high dynamic range features, DVS is faced with background noise problem. Spatiotemporal filter is an effective and hardware-friendly solution for DVS denoising but previous designs have large memory overhead or degraded performance issues. In this paper, we present a lightweight and real-time spatiotemporal denoising filter with set-associative cache-like memories, which has low space complexity of \text{O(m+n)} for DVS of $m\times n$ resolution. A two-stage pipeline for memory access with read cancellation feature is proposed to reduce power consumption. Further the bitwidth redundancy for event storage is exploited to minimize the memory footprint. We implemented our design on FPGA and experimental results show that it achieves state-of-the-art performance compared with previous spatiotemporal filters while maintaining low resource utilization and low power consumption of about 125mW to 210mW at 100MHz clock frequency.

翻译：动态视觉传感器（DVS）是一种新型神经形态成像设备，可产生异步事件。尽管具有高时间分辨率和高动态范围特性，DVS仍面临背景噪声问题。时空滤波器是DVS去噪的一种有效且硬件友好的解决方案，但先前设计存在内存开销大或性能下降的问题。本文提出一种轻量级、实时的时空去噪滤波器，采用组相联类缓存存储器，对分辨率为$m\times n$的DVS具有\text{O(m+n)}的低空间复杂度。提出了一种具有读取消功能的两级流水线存储器访问方案以降低功耗。此外，利用事件存储的位宽冗余最小化内存占用。我们在FPGA上实现了该设计，实验结果表明，与先前的时空滤波器相比，其在保持低资源利用率和低功耗（100MHz时钟频率下约125mW至210mW）的同时，实现了最先进的性能。