TRIP: Trainable Region-of-Interest Prediction for Hardware-Efficient Neuromorphic Processing on Event-based Vision

Cina Arjmand; Yingfu Xu; Kevin Shidqi; Alexandra F. Dobrita; Kanishkan Vadivel; Paul Detterer; Manolis Sifalakis; Amirreza Yousefzadeh; Guangzhi Tang

arXiv · 2406.17483

TRIP: Trainable Region-of-Interest Prediction for Hardware-Efficient Neuromorphic Processing on Event-based Vision

Cina Arjmand·Yingfu Xu·Kevin Shidqi·Alexandra F. Dobrita·Kanishkan Vadivel·Paul Detterer·Manolis Sifalakis·Amirreza Yousefzadeh·Guangzhi Tang

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Abstract

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.

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BibTeX RIS

Cina Arjmand, Yingfu Xu, Kevin Shidqi, Alexandra F. Dobrita, Kanishkan Vadivel, Paul Detterer, Manolis Sifalakis, Amirreza Yousefzadeh, Guangzhi Tang. 2024-06-25. TRIP: Trainable Region-of-Interest Prediction for Hardware-Efficient Neuromorphic Processing on Event-based Vision. https://arxiv.org/abs/2406.17483

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