Title: Neural and Synaptic Array Transceiver: A Brain-Inspired Computing Framework for Embedded Learning,
Abstract: Embedded, continual learning for autonomous and adaptive behavior is a key
application of neuromorphic hardware. However, neuromorphic implementations of
embedded learning at large scales that are both flexible and efficient have
been hindered by a lack of a suitable algorithmic framework. As a result, the
most neuromorphic hardware is trained off-line on large clusters of dedicated
processors or GPUs and transferred post hoc to the device. We address this by
introducing the neural and synaptic array transceiver (NSAT), a neuromorphic
computational framework facilitating flexible and efficient embedded learning
by matching algorithmic requirements and neural and synaptic dynamics. NSAT
supports event-driven supervised, unsupervised and reinforcement learning
algorithms including deep learning. We demonstrate the NSAT in a wide range of
tasks, including the simulation of Mihalas-Niebur neuron, dynamic neural
fields, event-driven random back-propagation for event-based deep learning,
event-based contrastive divergence for unsupervised learning, and voltage-based
learning rules for sequence learning. We anticipate that this contribution will
establish the foundation for a new generation of devices enabling adaptive
mobile systems, wearable devices, and robots with data-driven autonomy.