Spiking neural network encoding optimization via reinforcement learning and evolution strategy = 강화학습과 진화전략을 이용한 스파이킹 신경망 정보 부호화 최적화

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Spiking Neural Network, a neural network model used in Neuromorphic chips, is attracting attention as a next generation neural network model, but it is difficult to overcome low accuracy because of lack of a powerful learning algorithm like backpropagation. Recently, approach that converts Analog-valued Neural Networks trained using backpropagation to Spiking Neural Networks showed that Spiking Neural Network can obtain high accuracy comparable to Analog-valued Neural Network. However, this approach has a disadvantage of slowing the inference speed by encoding activation information into spike firing rates to obtain high accuracy. In order to tackle this problem, we converted the rate-encoded network into a network with efficient encoding scheme, Time-To-First encoding. The results of our study were as accurate as or better than those of the previous studies and the speed was faster than that of the rate-encoded networks. In addition, our model is a hardware-friendly model, which means this model is efficient to be implemented in Neuromorphic chips.
Advisors
Kim, Dae-Shikresearcher김대식researcher
Description
한국과학기술원 :전기및전자공학부,
Publisher
한국과학기술원
Issue Date
2019
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 전기및전자공학부, 2019.2,[iii, 25 p. :]

Keywords

Spiking neural network▼aneuromorphic enigneering▼aspike encoding▼areinforcement learning▼aevolution strategy; 스파이킹 신경망▼a신경모사 공학▼a스파이크 정보 부호화▼a강화 학습▼a진화 전략

URI
http://hdl.handle.net/10203/266711
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=843382&flag=dissertation
Appears in Collection
EE-Theses_Master(석사논문)
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