Neural and Synaptic Array Transceiver: A Brain-Inspired Computing Framework for Embedded Learning

• Published in: Frontiers in neuroscience Vol. 12; p. 583
• Main Authors: Detorakis, Georgios, Sheik, Sadique, Augustine, Charles, Paul, Somnath, Pedroni, Bruno U, Dutt, Nikil, Krichmar, Jeffrey, Cauwenberghs, Gert, Neftci, Emre
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 29.08.2018; Frontiers Media S.A
• Subjects: Algorithms; Artificial intelligence; Autonomy; Communication; Computational neuroscience; Embedded systems; event-based computing; Learning algorithms; Machine learning; Memory; Neural networks; neuromorphic algorithms; Neuromorphic computing; Neurons; Neuroscience; Noise; on-line learning; spiking neural networks; three-factor learning; La Jolla California; United States > US; California; spiking neural networks; on-line learning; three-factor learning; neuromorphic algorithms; event-based computing; Neuromorphic computing
• ISSN: 1662-4548; 1662-453X; 1662-453X
• DOI: 10.3389/fnins.2018.00583

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 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.