Neuromorphic circuit design using stochastic computation확률적 계산을 이용한 뉴로모픽 회로설계

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Neuromorphic circuits include complex blocks and SRAM to implement functions, which can be reduced to several gates by using stochastic computation (SC). SC is based on stochastic computation and represents a real number as a bit stream that consists of randomly shuffled 0s and 1s. To make bit streams, a lot of stochastic number generators (SNG) are utilized, which increases area and threatens benefit of SC. However, configuration of SNG should be changed carefully because it affects accuracy of SC. In this paper, LFSR sharing technique that based on random wiring within a subset of LFSR, is proposed. Proposed method reduces area and secures prediction accuracy, at the same time. From the experiment using 28nm library, circular shift and proposed method both reduce area by 86%, while degrade prediction accuracy 52% and 11%, respectively.
Advisors
Shin, Youngsooresearcher신영수researcher
Description
한국과학기술원 :전기및전자공학부,
Publisher
한국과학기술원
Issue Date
2016
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 전기및전자공학부, 2016.8 ,[iii, 32 p. :]

Keywords

Neuromorphic; Stochastic computation; Stochastic number generator; Area; Accuracy; 뉴로모픽; 확률 기반 연산법; 숫자열 생성기; 면적; 정확도

URI
http://hdl.handle.net/10203/221747
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=663454&flag=dissertation
Appears in Collection
EE-Theses_Master(석사논문)
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