On-chip Few-shot Learning with Surrogate Gradient Descent on a Neuromorphic Processor

Recent work suggests that synaptic plasticity dynamics in biological models of neurons and neuromorphic hardware are compatible with gradient-based learning (Neftci et al., 2019). Gradient-based learning requires iterating several times over a dataset, which is both time-consuming and constrains the training samples to be independently and identically distributed. This is incompatible with learning systems that do not have boundaries between training and inference, such as in neuromorphic hardware. One approach to overcome these constraints is transfer learning, where a portion of the network is pre-trained and mapped into hardware and the remaining portion is trained online. Transfer learning has the advantage that pre-training can be accelerated offline if the task domain is known, and few samples of each class are sufficient for learning the target task at reasonable accuracies. Here, we demonstrate on-line surrogate gradient few-shot learning on Intel's Loihi neuromorphic research processor using features pre-trained with spike-based gradient backpropagation-through-time. Our experimental results show that the Loihi chip can learn gestures online using a small number of shots and achieve results that are comparable to the models simulated on a conventional processor

Online Few-shot Gesture Learning on a Neuromorphic Processor

We present the Surrogate-gradient Online Error-triggered Learning (SOEL) system for online few-shot learningon neuromorphic processors. The SOEL learning system usesa combination of transfer learning and principles of computa-tional neuroscience and deep learning. We show that partiallytrained deep Spiking Neural Networks (SNNs) implemented onneuromorphic hardware can rapidly adapt online to new classesof data within a domain. SOEL updates trigger when an erroroccurs, enabling faster learning with fewer updates. Using gesturerecognition as a case study, we show SOEL can be used for onlinefew-shot learning of new classes of pre-recorded gesture data andrapid online learning of new gestures from data streamed livefrom a Dynamic Active-pixel Vision Sensor to an Intel Loihineuromorphic research processor.Comment: 10 pages, submitted to IEEE JETCAS for revie