Data_Sheet_1_Neural and Synaptic Array Transceiver: A Brain-Inspired Computing Framework for Embedded Learning.pdf (623.76 kB)
Download file

Data_Sheet_1_Neural and Synaptic Array Transceiver: A Brain-Inspired Computing Framework for Embedded Learning.pdf

Download (623.76 kB)
dataset
posted on 29.08.2018, 04:17 by Georgios Detorakis, Sadique Sheik, Charles Augustine, Somnath Paul, Bruno U. Pedroni, Nikil Dutt, Jeffrey Krichmar, Gert Cauwenberghs, Emre Neftci

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.

History

References