Miniature imaging systems are essential for space-constrained applications but are limited by memory and power constraints. While machine learning can reduce data size by extracting key features, its high energy demands often exceed the capacity of small batteries. This paper presents a CNN hardware accelerator optimized for object classification in miniature imaging systems. It processes data from a spatial Dynamic Vision Sensor (DVS), reconfigurable to a temporal DVS via pixel sharing, minimizing sensor area. By using ternary DVS outputs and a ternary-input, binary-weight neural network, the design reduces computation and memory needs. Fabricated in 28 nm CMOS, the accelerator cuts data size by 81% and MAC operations by 27%. It achieves 440 ms inference time at just 1.6 mW power consumption, improving the Figure-of-Merit (FoM) by 7.3x over prior CNN accelerators for miniature systems.

翻译：微型成像系统在空间受限的应用中至关重要，但受限于内存和功耗约束。虽然机器学习可通过提取关键特征减少数据量，但其高能耗需求常超出小型电池的供电能力。本文提出一种针对微型成像系统中物体分类任务优化的CNN硬件加速器。该加速器处理来自空间动态视觉传感器（DVS）的数据，该传感器可通过像素共享重构为时序DVS，从而最小化传感器面积。通过采用三元DVS输出及三元输入-二值权重的神经网络架构，该设计显著降低了计算与存储需求。基于28纳米CMOS工艺制造的加速器将数据量减少81%，乘累加运算量降低27%。在仅1.6毫瓦的功耗下实现440毫秒的推理时间，其性能指标（FoM）较现有微型系统CNN加速器提升7.3倍。