Presentation
Adaptive Spiking Neural Networks for Real-Time Multi-Object Detection Tasks
DescriptionProviding deterministic timing guarantees, beyond merely optimizing the accuracy-latency trade-off, is a mandatory yet unaddressed challenge for ultra-low-power spiking neural networks (SNNs) in resource-limited, safety-critical systems. In this paper, we propose RT-SNN, a novel adaptive SNN methods that integrates a system-level scheduling framework for SNN-based multi-object detection that, for the first time, co-optimizes inference accuracy while providing these strict timing guarantees. RT-SNN orchestrates SNN inference at both frame and timestep levels, introducing flexible timestep control and a novel membrane potential reuse mechanism to enhance accuracy without increasing latency. Evaluations on the KITTI dataset show that RT-SNN significantly improves the accuracy and energy efficiency compared to both state-of-the-art SNNs and traditional ANNs. Furthermore, a case study on a ROS-based F1/10 autonomous vehicle testbed demonstrates its real-time efficacy, validating its practical deployment in safety-critical systems.
Event Type
Research Manuscript
TimeMonday, July 272:34pm - 2:47pm PDT
LocationMtg Room 203AB