Few-Shot Transfer Learning for Individualized Braking Intent Detection on Neuromorphic Hardware
<|reference_start|>Few-Shot Transfer Learning for Individualized Braking Intent Detection on Neuromorphic Hardware: 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. The efficacy of the method is studied on an advanced driver assist system related task of predicting braking intention. 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 an energy reduction of over 97% with only a 1.3x increase in latency when using the Akida AKD1000 processor for network inference compared to an Intel Xeon CPU. Similar results were obtained in a subsequent ablation study using a subset of five out of 19 channels. Significance: Especially relevant to real-time applications, this work presents an energy-efficient, few-shot transfer learning method that is implemented on a neuromorphic processor capable of training a CSNN as new data becomes available, operating conditions change, or to customize group-level models to yield personalized models unique to each individual.<|reference_end|>
@article{lutes2024few-shot,
    title={Few-Shot Transfer Learning for Individualized Braking Intent Detection
  on Neuromorphic Hardware},
    author={Nathan Lutes, Venkata Sriram Siddhardh Nadendla, K. Krishnamurthy},
    journal={arXiv preprint arXiv:2408.03336},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.03336},
    primaryClass={cs.NE cs.LG eess.SP}
}