eprintid: 3676
rev_number: 10
eprint_status: archive
userid: 375
dir: disk0/00/00/36/76
datestamp: 2019-12-20 04:33:22
lastmod: 2019-12-20 04:33:55
status_changed: 2019-12-20 04:33:55
type: conference_item
metadata_visibility: no_search
creators_name: Ikechukwu, Ogbodo Mark
creators_name: Vu, The H.
creators_name: Dang, Nam Khanh
creators_name: Abdallah, Abderazek Ben
creators_id: dnk0904@gmail.com
creators_id: benab@u-aizu.ac.jp
title: Light-weight Spiking Neuron Processing Core for
Large-scale 3D-NoC based Spiking Neural Network
Processing Systems
ispublished: inpress
subjects: ECE
divisions: lab_sis
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%.
date: 2020-02
date_type: completed
official_url: http://www.bigcomputing.org/
full_text_status: restricted
pres_type: lecture
event_title: The 7th IEEE International Conference on Big Data and Smart Computing
event_location: Jeju, Korea
event_type: conference
refereed: TRUE
citation:   Ikechukwu, Ogbodo Mark and Vu, The H. and Dang, Nam Khanh and Abdallah, Abderazek Ben  (2020) Light-weight Spiking Neuron Processing Core for Large-scale 3D-NoC based Spiking Neural Network Processing Systems.  In: The 7th IEEE International Conference on Big Data and Smart Computing, Jeju, Korea.    (In Press)  
document_url: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3676/1/Light-weight%20Spiking%20Neuron%20Processing%20Core%20for%20Large-scale%203D-NoC%20based%20Spiking%20Neural%20Network%20Processing%20Systems.pdf