Zobrazeno 1 - 10 of 139 pro vyhledávání: '"Sunggu Lee"'
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Akademický článek
Selective Deep Convolutional Neural Network for Low Cost Distorted Image Classification
Autor: Minho Ha, Younghoon Byun, Jeonghun Kim, Jaecheol Lee, Youngjoo Lee, Sunggu Lee
Publikováno v: IEEE Access, Vol 7, Pp 133030-133042 (2019)
Neural networks trained using images with a certain type of distortion should be better at classifying test images with the same type of distortion than generally-trained neural networks, given other factors being equal. Based on this observation, an
Externí odkaz: https://doaj.org/article/e9d3d919a79640779ff93d913130079d
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5
Approximate Square Root Circuits with Low Latency and Power Dissipation
Autor: Sunggu Lee, Duhwan Kim
Publikováno v: Electronics, Vol 11, Iss 46, p 46 (2022)
Electronics; Volume 11; Issue 1; Pages: 46
This paper proposes a series of approximate square root circuit designs with high accuracy, low latency, low area, and low power dissipation requirements. The proposed designs are constructed using an array of controlled add–subtract cell elements
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a389774d6e72bd3de14ff998345a1f3e
https://www.mdpi.com/2079-9292/11/1/46
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6
Layerwise Buffer Voltage Scaling for Energy-Efficient Convolutional Neural Network
Autor: Minho Ha, Sunggu Lee, Seungsik Moon, Youngjoo Lee, Younghoon Byun
Publikováno v: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 40:1-10
In order to effectively reduce buffer energy consumption, which constitutes a significant part of the total energy consumption in a convolutional neural network (CNN), it is useful to apply different amounts of energy conservation effort to the diffe
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::84ea88ac08ecbcab202fe471d4d68546
https://doi.org/10.1109/tcad.2020.2992527
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7
Supervised Contrastive Learning for Voice Activity Detection
Autor: YoungJun Heo, Sunggu Lee
Publikováno v: Electronics
Volume 12
Issue 3
Pages: 705
The noise robustness of voice activity detection (VAD) tasks, which are used to identify the human speech portions of a continuous audio signal, is important for subsequent downstream applications such as keyword spotting and automatic speech recogni
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5ad81b654cc326791618b9e09e48f0f3
https://doi.org/10.3390/electronics12030705
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8
Memory-Reduced Network Stacking for Edge-Level CNN Architecture With Structured Weight Pruning
Autor: Jongmin Park, Younghoon Byun, Seungsik Moon, Sunggu Lee, Youngjoo Lee
Publikováno v: IEEE Journal on Emerging and Selected Topics in Circuits and Systems. 9:735-746
This paper presents a novel stacking and multi-level indexing scheme for convolutional neural networks (CNNs) used in energy-limited edge-level systems. Basically, the proposed scheme offers multiple accuracy modes by adopting a structured weight pru
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::a399f46bb367054a381ca14997121137
https://doi.org/10.1109/jetcas.2019.2952137
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10
Hierarchical Approximate Memory for Deep Neural Network Applications
Autor: Minho Ha, Seokha Hwang, Sunggu Lee, Youngjoo Lee, Jeonghun Kim
Publikováno v: ACSSC
Power consumed by a computer memory system can be significantly reduced if a certain level of error is permitted in the data stored in memory. Such an approximate memory approach is viable for use in applications developed using deep neural networks
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::22faee4a1cc580b424c382d4fe857d78
https://doi.org/10.1109/ieeeconf51394.2020.9443540
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