# Real Time Scheduling Framework for Multi Object Detection via Spiking Neural Networks

Source: [Real Time Scheduling Framework for Multi Object Detection via Spiking Neural Networks](<https://www.sota2.com/research/paper/68fa76ce1e209131cea6f32c>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T04:03:16.343Z

Authors: Donghwa Kang, Woojin Shin, Cheol-Ho Hong, Minsuk Koo, Brent ByungHoon Kang, Jinkyu Lee, Hyeongboo Baek

Published: 2025-01-29T15:16:46.000Z

## Abstract

Given the energy constraints in autonomous mobile agents (AMAs), such as unmanned vehicles, spiking neural networks (SNNs) are increasingly favored as a more efficient alternative to traditional artificial neural networks. AMAs employ multi-object detection (MOD) from multiple cameras to identify nearby objects while ensuring two essential objectives, (R1) timing guarantee and (R2) high accuracy for safety. In this paper, we propose RT-SNN, the first system design, aiming at achieving R1 and R2 in SNN-based MOD systems on AMAs. Leveraging the characteristic that SNNs gather feature data of input image termed as membrane potential, through iterative computation over multiple timesteps, RT-SNN provides multiple execution options with adjustable timesteps and a novel method for reusing membrane potential to support R1. Then, it captures how these execution strategies influence R2 by introducing a novel notion of mean absolute error and membrane confidence. Further, RT-SNN develops a new scheduling framework consisting of offline schedulability analysis for R1 and a run-time scheduling algorithm for R2 using the notion of membrane confidence. We deployed RT-SNN to Spiking-YOLO, the SNN-based MOD model derived from ANN-to-SNN conversion, and our experimental evaluation confirms its effectiveness in meeting the R1 and R2 requirements while providing significant energy efficiency.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2501.18412>)

[PDF](<https://arxiv.org/pdf/2501.18412.pdf>)