(A) variation-tolerant convolutional neural network processor with energy-efficient analog computation에너지 효율적인 아날로그 연산 기반의 변화에 둔감한 CNN 프로세서

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In this work, we propose an energy-efficient analog convolutional neural network (CNN) processor which is tolerant to variations. CNN is a machine learning algorithm that shows high accuracy in applications such as image recognition and data classification. One of the characteristics of CNN is that it consumes a lot of power due to a large number of MAC (Multiply-and-accumulate) operations. Since the demand for energy-efficient CNN processors to be processed at the edge device is increasing recently, CNN processors have been designed using analog circuit-based MAC operators instead of digital circuit-based MAC operators. However, there are two major problems with conventional CNN processors which use analog computation. First, large power consumption due to frequent analog-to-digital and digital-to-analog data conversion between interim layers of CNN limits the total energy efficiency of the CNN processor. Second, since the analog circuit inherently suffers from the PVT variations, the performance of the processor is degraded by the variations. To address the two issues of the conventional CNN processors, analog datapath which includes analog memory is proposed to eliminate the power overhead of A/D and D/A conversion and variation-tolerant computation and write-with-feedback are proposed to eliminate the variation dependency of the computation.
Advisors
Cho, SeongHwanresearcher조성환researcher
Description
한국과학기술원 :전기및전자공학부,
Publisher
한국과학기술원
Issue Date
2023
Identifier
325007
Language
eng
Description

학위논문(박사) - 한국과학기술원 : 전기및전자공학부, 2023.2,[v, 98 p. :]

Keywords

Machine learning▼aMachine learning processor▼aAnalog computing▼aNeural network; 기계 학습▼a기계 학습 프로세서▼a아날로그 연산▼a인공 신경망

URI
http://hdl.handle.net/10203/309189
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1030554&flag=dissertation
Appears in Collection
EE-Theses_Ph.D.(박사논문)
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