BRN (ASX) 2020 BRN Discussion

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SNU Scientists Join Forces with Samsung Technicians
April 2, 2018
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Open Science Lecture of SNU College of Natural Sciences
On March 27, the SNU College of Natural Sciences announced that it has teamed up with the Samsung Advanced Institute of Technology. 20 SNU professors of various fields in the general sciences will collaborate with the “brains” of Samsung to develop new technology that will shape the future of Korean industry.
This collaboration will be organized into five project teams of three to five professors. Professor Char Kookrin and Lee Takhee of the Department of Physics &Astronomy, Professor Lee Dongwhan of the Department of Chemistry, Professor Kang Myungjoo of the Department of
Mathematical Sciences, and Professor Kim Yongdai of the Department of Statistics will lead each project. In addition to research, the professors will participate in regular seminars and forums. They will also be eligible to spend their sabbaticals at the Samsung Advanced Institute of Technology, an extraordinary gesture from a company long known to be reticent about opening its doors to the public.
In choosing the College of Natural Sciences as its partner, Samsung signals its support of the pure sciences and its will to invest in long-term future technology rather than just focusing on short-term results. This will also benefit the SNU College of Natural Sciences, which has excelled in theoretical research but has had less of a history of industry collaboration.
Whereas in the past companies have tended to fund and utilize university research, this collaboration marks the beginning of a new era of cooperation between industry and academia. Professor Kim Seongkeun, Dean of the SNU College of Natural Sciences, commented, “The collaboration is a valuable opportunity for the university and the industry to work as one in building the technology of the future.”

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The 2019 International Joint Conference on Neural Networks (IJCNN) will be held atthe InterContinental Budapest Hotel in Budapest, Hungary on July 14-19, 2019.

the following paper was presented:

11:00AM A Spiking Neural Network with a Global Self-Controller for Unsupervised Learning Based on Spike-Timing-Dependent Plasticity Using Flash Memory Synaptic Devices [#19979]
Won-Mook Kang, Chul-Heung Kim, Soochang Lee, Sung Yun Woo, Jong-Ho Bae, Byung-Gook Park and Jong-Ho Lee, Seoul National University, Korea (South)

Neuromorphic engineering aims to implement a brain-inspired computing architecture as an alternative paradigm to the von Neumann processor. In this work, we propose a hardware-based spiking neural network (SNN) architecture for unsupervised learning with spike-timing-dependent plasticity (STDP) synapse array using flash memory synaptic array. This novel architecture includes a global self-controller to make each neuron in a single neuron layer operate systematically, which can be also an excellent benefit in terms of area required for system configuration. Therefore, the proposal of this architecture configuration is significant in terms of suggesting a methodology for extending a single neuron into a network. We perform circuit simulation using HSPICE to verify systematic operations of multiple neuron system such as feed-forward and -back pulses generation, a refractory period, a lateral inhibition, and a homeostasis. Various operation in the proposed architecture are designed based on MATLAB simulation result of 28 x 28 MNIST handwritten digit learning and recognition in the SNN having an array of thin-film transistor (TFT)- type NOR flash memory synaptic devices. The results of circuit simulation reflect the specifications required for the STDP operation using the long-term potentiation (LTP) and long-term depression (LTD) characteristics of the proposed synaptic device. The pulse scheme required for STDP in this paper is shown to be suitable for unsupervised learning with flash memory synaptic device