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An Efficient Event-driven Neuromorphic Architecture for Deep Spiking Neural Networks

Nguyen, Duy Anh and Bui, Duy Hieu and Iacopi, Francesca and Tran, Xuan Tu (2019) An Efficient Event-driven Neuromorphic Architecture for Deep Spiking Neural Networks. In: 2019 32nd IEEE International System-on-Chip Conference (SOCC), 3-6 September 2019, Singapore.

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Deep Neural Networks (DNNs) have been successfully applied to various real-world machine learning applications. However, performing large DNN inference tasks in real-time remains a challenge due to its substantial computational costs. Recently, Spiking Neural Networks (SNNs) have emerged as an alternative way of processing DNN’s task. Due to its eventbased, data-driven computation, SNN reduces both inference latency and complexity. With efficient conversion methods from traditional DNN, SNN exhibits similar accuracy, while leveraging many state-of-the-art network models and training methods. In this work, an efficient neuromorphic hardware architecture for image recognition task is presented. To preserve accuracy, the analog-to-spiking conversion algorithm is adopted. The system aims to minimize hardware area cost and power consumption, enabling neuromorphic hardware processing in edge devices. Simulation results have shown that, with the MNIST digit recognition task, the system has achieved ×20 reduction in terms of core area cost compared to the state-of-the-art works, with an accuracy of 94.4%, core area of 15 μm2 at a maximum frequency of 250 MHz.

Item Type: Conference or Workshop Item (Paper)
Subjects: Electronics and Communications
Electronics and Communications > Electronics and Computer Engineering
Divisions: Faculty of Electronics and Telecommunications (FET)
Key Laboratory for Smart Integrated Systems (SISLAB)
Depositing User: Prof. Xuan-Tu Tran
Date Deposited: 06 Dec 2019 08:24
Last Modified: 06 Dec 2019 08:24

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