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

• URL: http://arxiv.org/abs/2408.03336v2
• Date: Sun, 27 Oct 2024 22:52:33 GMT
• Title: Few-Shot Transfer Learning for Individualized Braking Intent Detection on Neuromorphic Hardware
• Authors: Nathan Lutes, Venkata Sriram Siddhardh Nadendla, K. Krishnamurthy,
• Abstract summary: This work explores use of a few-shot transfer learning method to train and implement a convolutional spiking neural network (CSNN) on a BrainChip. Results show the energy-efficiency of the neuromorphic hardware through a power reduction of over 97% with only a $1.3* increase in latency.
• Score: 0.21847754147782888
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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