Approaches to the design of machine learning system : 기계학습 시스템 설계를 위한 방법

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공과대학 전기·컴퓨터공학부
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서울대학교 대학원
Machine learningStochastic computingEnsemble learningDeep learningDeep neural networks
학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2016. 2. 최기영.
Machine learning has been paid attention because intelligence such as recognition, decision making, and recommendation is a helpful utility in industrial, medical, transportation, entertainment systems, and others that human need to interact with. As machine learning techniques are extensively applied to various areas, the needs for more robust algorithms and more efficient hardware have been increased. In order to develop an efficient machine learning system, we have researched from high-level algorithm down to low-level hardware logic
the main focus of our work is on ensemble machine learning and stochastic computing (SC).
The first work is to combine multiple components, i.e., multiple feature extractors (FE) and multiple classifiers in the aspect of pattern recognition. Ensemble of multiple components is one of challenging approaches for constructing a more accurate classifier. It can handle difficult problems where a single classifier easily makes a wrong decision due to lack of training or parameter optimization. Combining the decisions of participating classifiers statistically reduces the risk of wrong decision. We suggest a hierarchical ensemble framework of multiple feature extractors and multiple classifiers (MFMC).
The second work is to construct efficient hardware building blocks for machine learning in order to reduce system complexity and generate high area- and energy-efficient logic, where we exploit the property of machine learning systems that does not require accurate computations. We select stochastic computing (SC), which is an alternative paradigm to conventional binary arithmetic computing. SC can boost efficiency in terms of area, power, and error tolerance, while relaxing the accuracy of computation.
The third work is to combine both machine learning and stochastic computing, where we select deep learning. This work presents an efficient DNN design with stochastic computing. Observing that directly adopting stochastic computing to DNN has some challenges including random error fluctuation, range limitation, and overhead in accumulation, we address these problems by removing near-zero weights, applying weight-scaling, and integrating the activation function with the accumulator. The approach allows an easy implementation of early decision termination with a fixed hardware design by exploiting the progressive precision characteristics of stochastic computing, which was not easy with existing approaches. Experimental results show that our approach outperforms the conventional binary logic in terms of gate area, latency, and power consumption.
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College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Electrical and Computer Engineering (전기·정보공학부)Theses (Ph.D. / Sc.D._전기·정보공학부)
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