VPRTempo: A Fast Temporally Encoded Spiking Neural Network for Visual Place Recognition

TL;DR

VPRTempo is a fast, energy-efficient spiking neural network designed for visual place recognition, capable of real-time operation on resource-limited robotic systems, with training in minutes and inference in milliseconds.

Contribution

This work introduces VPRTempo, a novel SNN that uses temporal coding for efficient and rapid visual place recognition, outperforming prior SNNs in speed and maintaining competitive accuracy.

Findings

Achieves over 50 Hz inference speed on CPU

Maintains accuracy comparable to prior SNNs and NetVLAD

Reduces training time to minutes and inference to milliseconds

Abstract

Spiking Neural Networks (SNNs) are at the forefront of neuromorphic computing thanks to their potential energy-efficiency, low latencies, and capacity for continual learning. While these capabilities are well suited for robotics tasks, SNNs have seen limited adaptation in this field thus far. This work introduces a SNN for Visual Place Recognition (VPR) that is both trainable within minutes and queryable in milliseconds, making it well suited for deployment on compute-constrained robotic systems. Our proposed system, VPRTempo, overcomes slow training and inference times using an abstracted SNN that trades biological realism for efficiency. VPRTempo employs a temporal code that determines the timing of a single spike based on a pixel's intensity, as opposed to prior SNNs relying on rate coding that determined the number of spikes; improving spike efficiency by over 100%. VPRTempo is trained using Spike-Timing Dependent Plasticity and a supervised delta learning rule enforcing that each output spiking neuron responds to just a single place. We evaluate our system on the Nordland and Oxford RobotCar benchmark localization datasets, which include up to 27k places. We found that VPRTempo's accuracy is comparable to prior SNNs and the popular NetVLAD place recognition algorithm, while being several orders of magnitude faster and suitable for real-time deployment -- with inference speeds over 50 Hz on CPU. VPRTempo could be integrated as a loop closure component for online SLAM on resource-constrained systems such as space and underwater robots.