Unsupervised feature learning by pre-route simulation of auto-encoder behavior model = 자동 암호기 행동 모형의 경로결정 전 모의실험을 통한 자율 특징학습

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This paper assesses the feasibility of deep learning hardware by demonstrating an auto-encoder behavior model on pre-route simulation. Many recent deep learning literature has focused on learning high-level abstraction of unlabeled raw data by unsupervised fea-ture learning. However, the computation complexity and the slowness of the learning pro-cess due to large amounts of training data required better algorithms to be developed. Addi-tionally, using FPGAs rather than serial computing CPUs could efficiently overcome these computational drawbacks. However, the design effort for FPGA implementations of deep learning algorithms remains challenging and time consuming. In order to check the feasibil-ity of designing deep learning architectures on FPGA, we designed a behavior model of auto-encoder and performed pre-route simulation using VerilogHDL in MODELSIM. We successfully obtained a cycle-accurate result of the first hidden layer’s parameters. More specifically, we extracted latent representations of the hidden layer using the Kyoto natural images and remodeled MNIST image databases. Also, we evaluated the classification per-formance on MNIST by using the pre-route of the single auto-encoder and SOFTMAX classifier. This paper shows how pre-route simulation can help the designing process of unsupervised learning algorithms on hardware by providing the cycle-accurate result.
Advisors
Kim, Dae-Shikresearcher김대식
Description
한국과학기술원 : 전기및전자공학과,
Publisher
한국과학기술원
Issue Date
2014
Identifier
592424/325007  / 020124554
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 전기및전자공학과, 2014.8, [ vii, 57 p ]

Keywords

auto-encoder; 자율 특징학습; 결정 전 모의실험; 디지털 하드웨어 디자인; 행동 모형 모의실험; 자동 암호기; behavior model simulation; digital hardware design; pre-route simulation; unsupervised feature learning; neuromorphic algorithm; neuromorphic hardware

URI
http://hdl.handle.net/10203/196678
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=592424&flag=dissertation
Appears in Collection
EE-Theses_Master(석사논문)
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