DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Kim, Lee-Sup | - |
dc.contributor.advisor | 김이섭 | - |
dc.contributor.author | Choi, Myung-Hoon | - |
dc.date.accessioned | 2018-06-20T06:22:33Z | - |
dc.date.available | 2018-06-20T06:22:33Z | - |
dc.date.issued | 2017 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=675441&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/243333 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 전기및전자공학부, 2017.2,[iii, 41 p. :] | - |
dc.description.abstract | Applying highly accurate neural networks to mobile devices encounters energy problems in battery-limited mobile environments. To resolve these problems, neuromorphic hardware solutions that enable event-driven operation have been proposed. In this work, we present a novel sparse neuromorphic system that implements an E-I Net algorithm to further improve energy efficiency. We introduce a neuron clock-gating technique that significantly reduces energy consumption by predicting future neuron spike activity without any loss of accuracy. We also propose synaptic pruning to save additional energy with minimal impact on classification accuracy. For fast adaptation to a changing environment, a learning algorithm is implemented in the proposed system. Compared to prior studies, our experimental results illustrate that the proposed system achieves $6.9^×-15.0^×$ energy efficiency improvement with comparable accuracy. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Neural Network | - |
dc.subject | Sparse Spike | - |
dc.subject | Neuromorphic Computing | - |
dc.subject | Spiking Neural Network | - |
dc.subject | E-I Net | - |
dc.subject | 뉴럴 네트워크 | - |
dc.subject | 스파스 스파이크 | - |
dc.subject | 뉴로모픽 컴퓨팅 | - |
dc.subject | 스파이킹 뉴럴 네트워크 | - |
dc.subject | 이-아이 넷 | - |
dc.title | (An) energy-efficient sparse neuromorphic system with on-chip learning | - |
dc.title.alternative | 온 칩 러닝이 가능한 에너지 효율적인 스파스 뉴로모픽 시스템 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :전기및전자공학부, | - |
dc.contributor.alternativeauthor | 최명훈 | - |
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