AUTHOR=Detorakis Georgios , Sheik Sadique , Augustine Charles , Paul Somnath , Pedroni Bruno U. , Dutt Nikil , Krichmar Jeffrey , Cauwenberghs Gert , Neftci Emre TITLE=Neural and Synaptic Array Transceiver: A Brain-Inspired Computing Framework for Embedded Learning JOURNAL=Frontiers in Neuroscience VOLUME=12 YEAR=2018 URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2018.00583 DOI=10.3389/fnins.2018.00583 ISSN=1662-453X 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, most neuromorphic hardware are 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.