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ORCIDJinmook Lee
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2020 – today
- 2024
[c16]Fred Hohman
, Chaoqun Wang, Jinmook Lee, Jochen Görtler, Dominik Moritz, Jeffrey P. Bigham, Zhile Ren, Cecile Foret, Qi Shan, Xiaoyi Zhang:
Talaria: Interactively Optimizing Machine Learning Models for Efficient Inference. CHI 2024: 648:1-648:19- [i1]Fred Hohman, Chaoqun Wang, Jinmook Lee, Jochen Görtler, Dominik Moritz, Jeffrey P. Bigham, Zhile Ren, Cecile Foret, Qi Shan, Xiaoyi Zhang:
Talaria: Interactively Optimizing Machine Learning Models for Efficient Inference. CoRR abs/2404.03085 (2024) - 2020
- [j8]Jinsu Lee, Sanghoon Kang, Jinmook Lee, Dongjoo Shin, Donghyeon Han, Hoi-Jun Yoo:
The Hardware and Algorithm Co-Design for Energy-Efficient DNN Processor on Edge/Mobile Devices. IEEE Trans. Circuits Syst. 67-I(10): 3458-3470 (2020) - [j7]Kyuho Jason Lee, Jinmook Lee, Sungpill Choi, Hoi-Jun Yoo:
The Development of Silicon for AI: Different Design Approaches. IEEE Trans. Circuits Syst. 67-I(12): 4719-4732 (2020) - [c15]Sangyeob Kim, Juhyoung Lee, Sanghoon Kang, Jinmook Lee, Hoi-Jun Yoo:
A 146.52 TOPS/W Deep-Neural-Network Learning Processor with Stochastic Coarse-Fine Pruning and Adaptive Input/Output/Weight Skipping. VLSI Circuits 2020: 1-2
2010 – 2019
- 2019
- [j6]Jinmook Lee, Changhyeon Kim, Sanghoon Kang, Dongjoo Shin, Sangyeob Kim, Hoi-Jun Yoo:
UNPU: An Energy-Efficient Deep Neural Network Accelerator With Fully Variable Weight Bit Precision. IEEE J. Solid State Circuits 54(1): 173-185 (2019) - [j5]Donghyeon Han, Jinsu Lee, Jinmook Lee, Hoi-Jun Yoo:
A Low-Power Deep Neural Network Online Learning Processor for Real-Time Object Tracking Application. IEEE Trans. Circuits Syst. I Regul. Pap. 66-I(5): 1794-1804 (2019) - [c14]Jinsu Lee, Juhyoung Lee, Donghyeon Han, Jinmook Lee, Gwangtae Park, Hoi-Jun Yoo:
LNPU: A 25.3TFLOPS/W Sparse Deep-Neural-Network Learning Processor with Fine-Grained Mixed Precision of FP8-FP16. ISSCC 2019: 142-144 - [c13]Juhyoung Lee, Dongjoo Shin, Jinsu Lee, Jinmook Lee, Sanghoon Kang, Hoi-Jun Yoo:
A Full HD 60 fps CNN Super Resolution Processor with Selective Caching based Layer Fusion for Mobile Devices. VLSI Circuits 2019: 302- - [c12]Donghyeon Han, Jinsu Lee, Jinmook Lee, Hoi-Jun Yoo:
A 1.32 TOPS/W Energy Efficient Deep Neural Network Learning Processor with Direct Feedback Alignment based Heterogeneous Core Architecture. VLSI Circuits 2019: 304- - 2018
- [j4]Sanghoon Kang, Jinmook Lee, Kyeongryeol Bong, Changhyeon Kim, Youchang Kim, Hoi-Jun Yoo:
Low-Power Scalable 3-D Face Frontalization Processor for CNN-Based Face Recognition in Mobile Devices. IEEE J. Emerg. Sel. Topics Circuits Syst. 8(4): 873-883 (2018) - [j3]Dongjoo Shin, Jinmook Lee, Jinsu Lee, Juhyoung Lee, Hoi-Jun Yoo:
DNPU: An Energy-Efficient Deep-Learning Processor with Heterogeneous Multi-Core Architecture. IEEE Micro 38(5): 85-93 (2018) - [c11]Donghyeon Han, Jinsu Lee, Jinmook Lee, Sungpill Choi, Hoi-Jun Yoo:
A 141.4 mW Low-Power Online Deep Neural Network Training Processor for Real-time Object Tracking in Mobile Devices. ISCAS 2018: 1-5 - [c10]Jinmook Lee, Changhyeon Kim, Sanghoon Kang, Dongjoo Shin, Sangyeob Kim, Hoi-Jun Yoo:
UNPU: A 50.6TOPS/W unified deep neural network accelerator with 1b-to-16b fully-variable weight bit-precision. ISSCC 2018: 218-220 - [c9]Sanghoon Kang, Jinmook Lee, Changhyeon Kim, Hoi-Jun Yoo:
B-Face: 0.2 MW CNN-Based Face Recognition Processor with Face Alignment for Mobile User Identification. VLSI Circuits 2018: 137-138 - 2017
- [j2]Jinmook Lee, Seongwook Park, Injoon Hong, Hoi-Jun Yoo:
An Energy-Efficient Speech-Extraction Processor for Robust User Speech Recognition in Mobile Head-Mounted Display Systems. IEEE Trans. Circuits Syst. II Express Briefs 64-II(4): 457-461 (2017) - [c8]Jinmook Lee, Dongjoo Shin, Hoi-Jun Yoo:
A 21mW low-power recurrent neural network accelerator with quantization tables for embedded deep learning applications. A-SSCC 2017: 237-240 - [c7]Dongjoo Shin, Jinmook Lee, Jinsu Lee, Juhyoung Lee, Hoi-Jun Yoo:
An energy-efficient deep learning processor with heterogeneous multi-core architecture for convolutional neural networks and recurrent neural networks. COOL Chips 2017: 1-2 - [c6]Sanghoon Kang, Jinmook Lee, Kyeongryeol Bong, Changhyeon Kim, Hoi-Jun Yoo:
A 0.53mW ultra-low-power 3D face frontalization processor for face recognition with human-level accuracy in wearable devices. ISCAS 2017: 1-4 - [c5]Dongjoo Shin, Jinmook Lee, Jinsu Lee, Hoi-Jun Yoo:
14.2 DNPU: An 8.1TOPS/W reconfigurable CNN-RNN processor for general-purpose deep neural networks. ISSCC 2017: 240-241 - 2016
- [c4]Jinmook Lee, Seongwook Park, Injoon Hong, Hoi-Jun Yoo:
An 8.3mW 1.6Msamples/s multi-modal event-driven speech enhancement processor for robust speech recognition in smart glasses. ESSCIRC 2016: 117-120 - [c3]Seongwook Park, Sungpill Choi, Jinmook Lee, Minseo Kim, Junyoung Park, Hoi-Jun Yoo:
14.1 A 126.1mW real-time natural UI/UX processor with embedded deep-learning core for low-power smart glasses. ISSCC 2016: 254-255 - 2015
- [j1]Seongwook Park, Junyoung Park, Kyeongryeol Bong, Dongjoo Shin, Jinmook Lee, Sungpill Choi, Hoi-Jun Yoo:
An Energy-Efficient and Scalable Deep Learning/Inference Processor With Tetra-Parallel MIMD Architecture for Big Data Applications. IEEE Trans. Biomed. Circuits Syst. 9(6): 838-848 (2015) - [c2]Jinmook Lee, Seongwook Park, Injoon Hong, Hoi-Jun Yoo:
A 3.13nJ/sample energy-efficient speech extraction processor for robust speech recognition in mobile head-mounted display systems. ISCAS 2015: 1790-1793 - [c1]Seongwook Park, Kyeongryeol Bong, Dongjoo Shin, Jinmook Lee, Sungpill Choi, Hoi-Jun Yoo:
4.6 A1.93TOPS/W scalable deep learning/inference processor with tetra-parallel MIMD architecture for big-data applications. ISSCC 2015: 1-3
Coauthor Index
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