TRIP: Trainable Region-of-Interest Prediction for Hardware-Efficient Neuromorphic Processing on Event-based Vision
<|reference_start|>TRIP: Trainable Region-of-Interest Prediction for Hardware-Efficient Neuromorphic Processing on Event-based Vision: 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.<|reference_end|>
@article{arjmand2024trip:,
    title={TRIP: Trainable Region-of-Interest Prediction for Hardware-Efficient
  Neuromorphic Processing on Event-based Vision},
    author={Cina Arjmand, Yingfu Xu, Kevin Shidqi, Alexandra F. Dobrita, Kanishkan
  Vadivel, Paul Detterer, Manolis Sifalakis, Amirreza Yousefzadeh, Guangzhi
  Tang},
    journal={arXiv preprint arXiv:2406.17483},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.17483},
    primaryClass={cs.CV eess.IV}
}