Line-based Event Preprocessing: Towards Low-Energy Neuromorphic Computer Vision

TL;DR

This paper introduces a line-based event preprocessing method for neuromorphic vision systems that reduces energy consumption while maintaining or improving classification accuracy, advancing energy-efficient neuromorphic computing.

Contribution

The paper presents a novel line-based event preprocessing technique that enhances energy efficiency in neuromorphic vision systems without sacrificing accuracy.

Findings

Preprocessing reduces energy consumption significantly.

Classification accuracy is maintained or improved.

Method performs well across multiple benchmark datasets.

Abstract

Neuromorphic vision made significant progress in recent years, thanks to the natural match between spiking neural networks and event data in terms of biological inspiration, energy savings, latency and memory use for dynamic visual data processing. However, optimising its energy requirements still remains a challenge within the community, especially for embedded applications. One solution may reside in preprocessing events to optimise data quantity thus lowering the energy cost on neuromorphic hardware, proportional to the number of synaptic operations. To this end, we extend an end-to-end neuromorphic line detection mechanism to introduce line-based event data preprocessing. Our results demonstrate on three benchmark event-based datasets that preprocessing leads to an advantageous trade-off between energy consumption and classification performance. Depending on the line-based preprocessing strategy and the complexity of the classification task, we show that one can maintain or increase the classification accuracy while significantly reducing the theoretical energy consumption. Our approach systematically leads to a significant improvement of the neuromorphic classification efficiency, thus laying the groundwork towards a more frugal neuromorphic computer vision thanks to event preprocessing.