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An Efficient Software-Hardware Design Framework for Spiking Neural Network Systems

Dang, Nam Khanh and Abdallah, Abderazek Ben (2019) An Efficient Software-Hardware Design Framework for Spiking Neural Network Systems. In: The International Conference on Internet of Things, Embedded Systems and Communications (IINTEC 2019). (In Press)

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Spiking Neural Network (SNN) is the third generation of Neural Network (NN) mimicking the natural behavior of the brain. By processing based on binary input/output, SNNs offer lower complexity, higher density and lower power consumption. This work presents an efficient software-hardware design framework for developing SNN systems in hardware. In addition, a design of low-cost neurosynaptic core is presented based on packet-switching communication approach. The evaluation results show that the ANN to SNN conversion method with the size 784:1200:1200:10 performs 99% accuracy for MNIST while the unsupervised STDP archives 89% with the size 784:400 with recurrent connections. The design of 256-neurons and 65k synapses is also implemented in ASIC 45nm technology with an area cost of 0.205 mm2.

Item Type: Conference or Workshop Item (Paper)
Subjects: Electronics and Communications
Divisions: Key Laboratory for Smart Integrated Systems (SISLAB)
Depositing User: Khanh N. Dang
Date Deposited: 04 Jan 2020 05:42
Last Modified: 04 Jan 2020 05:42

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