CenBRAIN Neurotech's New Contribution in IEEE AICAS'24

The 6th IEEE International Conference on Artificial Intelligence Circuits and Systems (IEEE AICAS 2024) is being held in Abu Dhabi, UAE from April 22-25, 2024. This conference is a flagship event of IEEE Circuits and Systems Society and one of the most highly regarded annual conferences in this field.

Last year, CenBRAIN Neurotech Center of Excellence was the host organizer of the past edition (IEEE AICAS 2023), Chair Professor Mohamad Sawan, the founding director of CenBRAIN Neurotech, was the general chair of that conference edition.

This year,a paper titled “NeuroSORT: a Neuromorphic Accelerator for Spike-Based Online and Real-Time Tracking” was accepted for presentation and publication in this conference, which led byPh.D student Ziyang Shen.

Abstract

The increasing need for real-time computation with low power consumption is driving the advancement of specialized neuromorphic processors on various applications. Multi object tracking, as one of the most challenging tasks in computer vision, has gained wide attention and many mature solutions with full precision have been proposed. Nevertheless, they consume huge power and computation resources and fail to adapt to edge application scenarios with strict power and resource constraints. A solution with dedicated neuromorphic accelerators for online and real-time object tracking based on spike signals is urgently required. In this work, we propose NeuroSORT, a neuromorphic accelerator for spike-based online and real-time object tracking. NeuroSORT leverages spiking neural network (SNN) to solve linear assignment problem and explores the hardware acceleration on tracking algorithms. Experimental results show that the proposed accelerator reaches an accuracy of 99.43% on linear assignment task and 69.641 HOTA score on MOT17 dataset, while consuming 0.257mW energy and 0.17mm2 area. The overall power consumption is reduced by 41.1% compared with SOTA works with equivalent performance.

Fig. 1: A framework of Multi Object Tracking system.

Fig. 2: An overview of the proposed TDMPE unit.