Neural and Synaptic Array Transceiver: A Brain-Inspired Computing Framework for Embedded Learning
This work addresses the problem of enabling adaptive mobile systems and robots with data-driven autonomy, representing a novel method for a known bottleneck in neuromorphic computing.
The paper tackles the lack of a flexible and efficient algorithmic framework for embedded learning in neuromorphic hardware by introducing the Neural and Synaptic Array Transceiver (NSAT), which supports various learning algorithms and demonstrates capabilities in tasks like event-driven deep learning and sequence learning.
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.