@inproceedings{SisLab3907,
       booktitle = {The 7th IEEE International Conference on Big Data and Smart Computing},
           month = {February},
           title = {Light-weight Spiking Neuron Processing Core for Large-scale 3D-NoC based Spiking Neural Network Processing Systems},
          author = {Ogbodo Mark Ikechukwu and The H. Vu and Nam Khanh Dang and Abderazek Ben Abdallah},
            year = {2020},
             url = {https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3907/},
        abstract = {With the increasing demand for computing machines
that more closely model the biological brain, the field
of neuro-inspired computing has progressed to the exploration
of Spiking Neural Networks (SNN), and to best the challenges of
conventional Von Neumann architecture, several hardware-based
(neuromorphic) chips have been designed. A neuromorphic chip
is based on spiking neurons which process input information
only when they receive spike signals. Given a sparsely-distributed
input spike train, the power consumption for such event-driven
hardware would be reduced since large portions of the network
that are not driven by incoming spikes can be set into a powergated
mode. The challenges that need to be solved toward
building in hardware such a spiking neuromorphic chip with a
massive number of synapse include building small-sized spiking
neuro-cores with low-power consumption, efficient neurocoding
scheme, and lightweight on-chip learning algorithm. In this paper,
we present the hardware implementation and evaluation of a
light-weight spiking neuron processing core (SNPC) for our 3DNoC
SNN processor, and the design of its on-chip learning block.
The SNPC embeds 256 Leaky Integrate and Fire (LIF) neurons,
and crossbar based synapses, covering a chip area of 0.12mm2.
Its performance is evaluated using MNIST dataset, achieving an
inference accuracy of 97.55\%.}
}