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

Frontiers in Neuroscience

Sadique Sheik¹  Gert Cauwenberghs²  Bruno U. Pedroni²  Georgios Detorakis³  Nikil Dutt⁴  Emre Neftci⁴  Jeffrey Krichmar⁴  Charles Augustine⁵  Somnath Paul⁵ 

[1] Biocircuits Institute, University of California, San Diego, La Jolla, CA, United States;Department of Bioengineering and Institute for Neural Computation, University of California, San Diego, La Jolla, CA, United States;Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, United States;Department of Computer Science, University of California, Irvine, Irvine, CA, United States;Intel Corporation-Circuit Research Lab, Hillsboro, OR, United States;

关键词: Neuromorphic computing;    neuromorphic algorithms;    three-factor learning;    on-line learning;    event-based computing;    spiking neural networks;   

DOI  :  10.3389/fnins.2018.00583

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown