TY  - INPR
ID  - SisLab3916
UR  - http://www.iintec.org/
A1  - Dang, Nam Khanh
A1  - Abdallah, Abderazek Ben
Y1  - 2019/12/22/
N2  - 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.
TI  - An Efficient Software-Hardware Design Framework for Spiking Neural Network Systems
AV  - public
T2  - The International Conference on Internet of Things, Embedded Systems and Communications (IINTEC 2019)
ER  -