A Spike Learning System for Event-driven Object Recognition

• URL: http://arxiv.org/abs/2101.08850v1
• Date: Thu, 21 Jan 2021 20:57:53 GMT
• Title: A Spike Learning System for Event-driven Object Recognition
• Authors: Shibo Zhou, Wei Wang, Xiaohua Li, Zhanpeng Jin
• Abstract summary: Event-driven sensors such as LiDAR and dynamic vision sensor (DVS) have found increased attention in high-resolution and high-speed applications. We present a spiking learning system that uses the spiking neural network (SNN) with a novel temporal coding for accurate and fast object recognition.
• Score: 8.875351982997554
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Event-driven sensors such as LiDAR and dynamic vision sensor (DVS) have found increased attention in high-resolution and high-speed applications. A lot of work has been conducted to enhance recognition accuracy. However, the essential topic of recognition delay or time efficiency is largely under-explored. In this paper, we present a spiking learning system that uses the spiking neural network (SNN) with a novel temporal coding for accurate and fast object recognition. The proposed temporal coding scheme maps each event's arrival time and data into SNN spike time so that asynchronously-arrived events are processed immediately without delay. The scheme is integrated nicely with the SNN's asynchronous processing capability to enhance time efficiency. A key advantage over existing systems is that the event accumulation time for each recognition task is determined automatically by the system rather than pre-set by the user. The system can finish recognition early without waiting for all the input events. Extensive experiments were conducted over a list of 7 LiDAR and DVS datasets. The results demonstrated that the proposed system had state-of-the-art recognition accuracy while achieving remarkable time efficiency. Recognition delay was shown to reduce by 56.3% to 91.7% in various experiment settings over the popular KITTI dataset.

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