Neuromorphic processors are well-suited for efficiently handling sparse events from event-based cameras. However, they face significant challenges in the growth of computing demand and hardware costs as the input resolution increases. This paper proposes the Trainable Region-of-Interest Prediction (TRIP), the first hardware-efficient hard attention framework for event-based vision processing on a neuromorphic processor. Our TRIP framework actively produces low-resolution Region-of-Interest (ROIs) for efficient and accurate classification. The framework exploits sparse events' inherent low information density to reduce the overhead of ROI prediction. We introduced extensive hardware-aware optimizations for TRIP and implemented the hardware-optimized algorithm on the SENECA neuromorphic processor. We utilized multiple event-based classification datasets for evaluation. Our approach achieves state-of-the-art accuracies in all datasets and produces reasonable ROIs with varying locations and sizes. On the DvsGesture dataset, our solution requires 46x less computation than the state-of-the-art while achieving higher accuracy. Furthermore, TRIP enables more than 2x latency and energy improvements on the SENECA neuromorphic processor compared to the conventional solution.

本文提出了第一种硬件高效的基于神经形态处理器的硬注意力框架Trainable Region-of-Interest Prediction (TRIP)，该框架通过利用稀疏事件的低信息密度来减少ROI预测的开销。在多个基于事件的分类数据集上，该方法达到了最先进的准确性，并在DvsGesture数据集上实现了46倍的计算量减少和更高的准确性，同时相比传统解决方案，在SENECA神经形态处理器上实现了超过2倍的延迟和能耗改进。

TRIP: 可训练的感兴趣区域预测技术，用于基于事件视觉的硬件高效神经形态处理