Few-Shot Transfer Learning for Individualized Braking Intent Detection on Neuromorphic Hardware

TL;DR

This paper demonstrates a few-shot transfer learning approach to develop individualized braking intent detection models using neuromorphic hardware, achieving high accuracy and energy efficiency with minimal training data.

Contribution

It introduces a method for rapid adaptation of group-level models to individual users on neuromorphic hardware, reducing training time and power consumption.

Findings

Achieved over 90% accuracy with as few as three training epochs.

Reduced power consumption by over 97% on neuromorphic hardware.

Maintained high performance with a subset of EEG channels.

Abstract

Objective: This work explores use of a few-shot transfer learning method to train and implement a convolutional spiking neural network (CSNN) on a BrainChip Akida AKD1000 neuromorphic system-on-chip for developing individual-level, instead of traditionally used group-level, models using electroencephalographic data. Main Results: Efficacy of the above methodology to develop individual-specific braking intention predictive models by rapidly adapting the group-level model in as few as three training epochs while achieving at least 90% accuracy, true positive rate and true negative rate is presented. Further, results show the energy-efficiency of the neuromorphic hardware through a power reduction of over 97% with only a $1.3* increase in latency when using the Akida AKD1000 processor for network inference compared to an Intel Xeon central processing unit. Similar results were obtained in a subsequent ablation study using a subset of five out of 19 channels.