@inproceedings{SisLab3916,
       booktitle = {The International Conference on Internet of Things, Embedded Systems and Communications (IINTEC 2019)},
           month = {December},
           title = {An Efficient Software-Hardware Design Framework for Spiking Neural Network Systems},
          author = {Nam Khanh Dang and Abderazek Ben Abdallah},
            year = {2019},
             url = {https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3916/},
        abstract = {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.}
}