This paper describes a fully spike-based neural network for optical flow
estimation from Dynamic Vision Sensor data. A low power embedded implementation
of the method which combines the Asynchronous Time-based Image Sensor with
IBM's TrueNorth Neurosynaptic System is presented. The sensor generates spikes
with sub-millisecond resolution in response to scene illumination changes.
These spike are processed by a spiking neural network running on TrueNorth with
a 1 millisecond resolution to accurately determine the order and time
difference of spikes from neighboring pixels, and therefore infer the velocity.
The spiking neural network is a variant of the Barlow Levick method for optical
flow estimation. The system is evaluated on two recordings for which ground
truth motion is available, and achieves an Average Endpoint Error of 11% at an
estimated power budget of under 80mW for the sensor and computation.

该论文描述了一种完全基于脉冲的神经网络，用于从动态视觉传感器数据中估计光流。该神经网络是 Barlow Levick 方法的一种变体，可以与基于 TrueNorth 的神经元实现。该系统在地面真值运动可用的两个记录上进行评估，在传感器和计算的预估功率预算低于 80mW 的情况下，误差率平均终点为 11％。