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

Nathan Lutes; Venkata Sriram Siddhardh Nadendla; K. Krishnamurthy

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arXiv · 2408.03336

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

Nathan Lutes·Venkata Sriram Siddhardh Nadendla·K. Krishnamurthy

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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.

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Nathan Lutes, Venkata Sriram Siddhardh Nadendla, K. Krishnamurthy. 2024-07-21. Few-Shot Transfer Learning for Individualized Braking Intent Detection on Neuromorphic Hardware. https://arxiv.org/abs/2408.03336

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