SHS Web Conf.
Volume 77, 2020The 2nd ACM Chapter International Conference on Educational Technology, Language and Technical Communication (ETLTC2020)
|Number of page(s)||2|
|Section||Topics in Computer Science|
|Published online||08 May 2020|
Architecture and Design of a Spiking Neuron Processor Core Towards the Design of a Large-scale Event-Driven 3D-NoC-based Neuromorphic Processor
Adaptive Systems Laboratory, Graduate School of Computer Science and Engineering, The University of Aizu, Japan.
* Corresponding author e-mail: firstname.lastname@example.org
Neuromorphic computing tries to model in hardware the biological brain which is adept at operating in a rapid, real-time, parallel, low power, adaptive and fault-tolerant manner within a volume of 2 liters. Leveraging the event driven nature of Spiking Neural Network (SNN), neuromorphic systems have been able to demonstrate low power consumption by power gating sections of the network not driven by an event at any point in time. However, further exploration in this field towards the building of edge application friendly agents and efficient scalable neuromorphic systems with large number of synapses necessitates the building of small-sized low power spiking neuron processor core with efficient neuro-coding scheme and fault tolerance. This paper presents a spiking neuron processor core suitable for an event-driven Three-Dimensional Network on Chip (3D-NoC) SNN based neuromorphic systems. The spiking neuron Processor core houses an array of leaky integrate and fire (LIF) neurons, and utilizes a crossbar memory in modelling the synapses, all within a chip area of 0.12mm2 and was able to achieves an accuracy of 95.15% on MNIST dataset inference.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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