Full metadata record
DC Field | Value | Language |
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dc.contributor.advisor | Hwang, Jae Youn | - |
dc.contributor.advisor | Minkyu Je | - |
dc.contributor.author | Lim, Hee Sung | - |
dc.date.accessioned | 2017-05-10T08:52:47Z | - |
dc.date.available | 2016-02-12T00:00:00Z | - |
dc.date.issued | 2016 | - |
dc.identifier.uri | http://dgist.dcollection.net/jsp/common/DcLoOrgPer.jsp?sItemId=000002227453 | en_US |
dc.identifier.uri | http://hdl.handle.net/20.500.11750/1461 | - |
dc.description.abstract | The need for human-machine interaction such as speech and gesture recognition has steadily grown in wearable devices. As applications become more intelligent such as facial emotion recognition, a variety of recognition algorithms has been developed and evolving. However, as the recognition algorithms become more complex, the more computation is required to perform the application in a limited battery capacity of wearable devices, which means that energy-efficiency is critical issue. In this thesis, one of the widely used recognition algorithm, artificial neural network (ANN), is selected as a target algorithm and its characteristic, inherent algorithmic fault tolerance (AFT), is adopted to improve energy-efficiency. To compute the recognition algorithm (ANN), Significant-driven iterative approximate multiplier (SDIAM) is utilized. Motivated by the fact that both an iteration of multiplication and the number of hidden nodes play key roles for a trade-off between recognition accuracy and energy consumption, these two parameters are optimized for a minimum of energy consumption of ANN, allowing acceptable recognition accuracy. The evaluation shows that the joint optimization between the iteration of multiplication and the number of hidden nodes save 70% of the energy consumption, compared with using precise computation, at the same recognition accuracy target for both handwritten and isolated spoken digit recognition. Furthermore, adopting SDIAM in training phase, the recognition accuracy is more improved, which leads to 87% and 75% lower energy consumption for handwritten and isolated spoken digit recognition. ⓒ 2016 DGIST | - |
dc.description.tableofcontents | I. Introduction 1-- II. Recognition Applications on Wearable Devices 4-- 2.1. Trend on Recognition Applications 4-- 2.1.1. Vision-Based Applications 4-- 2.1.2. Sound-Based Application 5-- 2.1.3. Other Sensor-Based Application 6-- 2.2. Recognition Algorithms for Application 6-- 2.2.1. Artificial Neural Network (ANN) 7-- 2.2.2. Support Vector Machine (SVM) 8-- 2.2.3. Hidden Markov Model (HMM) 9-- 2.2.4. Deep Learning 10-- III. Artificial Neural Network 12-- 3.1 Introduction to ANN 12-- 3.2 Architecture and Feedforward Operation of ANN 12-- 3.3 Learning Algorithm 14-- 3.4 A demand for selecting the optimal number of hidden nodes 14-- IV. Energy-Efficient Hardware Accelerator for ANN 16-- 4.1 Characteristics of ANN to apply SDIAM 16-- 4.2 Significance-Driven Iterative Approximate Multiplier (SDIAM) 16-- 4.2.1. Architecture and Operation of SDIAM 16-- 4.2.2. Recognition Accuracy and Energy Consumptions 19-- 4.3. Joint Optimization of N and n 19-- 4.4 Training ANN with SDIAM 20-- V. Performance Evaluation 22-- 5.1. Evaluation Methodology 22-- 5.2. Evaluation 22-- VI. Related Works 30-- VII. Conclusion 32 |
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dc.format.extent | 36 | - |
dc.language | eng | - |
dc.publisher | DGIST | - |
dc.subject | artificial neural network (ANN) | - |
dc.subject | significant-driven iterative approximate multiplier (SDIAM) | - |
dc.subject | handwritten digit recognition | - |
dc.subject | isolated spoken digit recognition. 경망 | - |
dc.subject | 곱셈기 | - |
dc.subject | 최적화 | - |
dc.subject | 문자인식 | - |
dc.subject | 음성인식 | - |
dc.title | Joint Optimization of Computational Accuracy and Algorithm Parameters for Energy-Efficient Recognition Algorithm | - |
dc.title.alternative | 에너지 효율적 인식알고리즘을 위한 알고리즘 파라매터와 계산의 정확도의 최적화 | - |
dc.type | Thesis | - |
dc.identifier.doi | 10.22677/thesis.2227453 | - |
dc.description.alternativeAbstract | 문자인식, 음성인식과 같은 사람-기계간의 상호작용을 위한 기술은 웨어러블 디바이스에서 매우 중요하다. 이러한 인식기술을 위해 다양한 인식알고리즘이 개발되어 왔고 많은 연구자들에 의해 진보하고 있다. 하지만, 더 복잡한 문제들을 더 정확하게 수행함으로 디바이스는 알고리즘을 수행하기 위해 많은 계산과 에너지를 필요로 하고 있다. 본 논문에서는 많이 사용되는 인식기술인 인공신경망을 사용하고 그의 특성인 알고리즘 결함 허용을 이용하고 에너지 효율적 곱셈기인 SDIAM 의 파라매터를 통해 에너지소비를 줄인다. 인식알고리즘을 연산하기 위해 SDIAM 이 사용되었다. 알고리즘에서는 은닉노드의 수, 그리고 곱셈기에서는 Iteration 의 수를 가변하며 충분한 정확도를 갖는 최소 에너지 소비 지점을 찾기 위한 실험을 진행하였다. 본 논문의 평가결과는 계산정확도와 알고리즘 파라매터의 최적화를 통해서 기존의 정확한 곱셈기와 비교하여 문자인식과 음성인식에 있어 동일한 70%의 에너지를 줄였다. 더 나아가, SDIAM 을 사용하여 학습단계에서 알고리즘을 연산했을 때 인식의 정확도는 더 향상되었고 에너지 소비를 더욱 줄일 수 있었다(87%-문자인식, 75%-음성인식). ⓒ 2016 DGIST | - |
dc.description.degree | Master | - |
dc.contributor.department | Information and Communication Engineering | - |
dc.contributor.coadvisor | Park, Tae Joon | - |
dc.date.awarded | 2016. 2 | - |
dc.publisher.location | Daegu | - |
dc.description.database | dCollection | - |
dc.date.accepted | 2016-02-12 | - |
dc.contributor.alternativeDepartment | 대학원 정보통신융합공학전공 | - |
dc.contributor.affiliatedAuthor | Lim, Hee Sung | - |
dc.contributor.affiliatedAuthor | Hwang, Jae Youn | - |
dc.contributor.affiliatedAuthor | Minkyu Je | - |
dc.contributor.affiliatedAuthor | Park, Tae Joon | - |
dc.contributor.alternativeName | 임희성 | - |
dc.contributor.alternativeName | 황재윤 | - |
dc.contributor.alternativeName | 제민규 | - |
dc.contributor.alternativeName | 박태준 | - |