Design and Implementation of Digital Spiking Neurons for Ultra-low-Power In-cluster processors

Abstract: Neuromorphic computing is a recent and growing field of research. Its conceptual attractiveness is due to the potential it has in deep learning applications such as sensor networks, low-power computer vision, robotics and other fields. Inspired by the functioning of brain, different neural network models have been devised, each with their own special focus on certain applications. Using such computing models are already helping us in different cases such as image, character and voice recognition, data analysis, stock market prediction, etc. Among the multitude of artificial neural models available, spiking neurons are more deeply inspired by biological neural networks. Leaky, Integrate and Fire (LIF) neuron model is one such model that can reproduce a good number of functions, be simple and also extensible in structure. Current deep learning applications are tied to servers and datacenters for their power and resource hungry existence. This work aims at building a low power neuron core taking advantage of LIF neuron, that could possible result in independent battery powered devices. A hardware design of LIF neuron based scalable neural core is explored, constructed and analysis for power consumption is made.