  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.
