Jinmook Lee

25 papers A* 1C 3Journal 9Unranked 12
YearRankTypeTitle / Venue / Authors
2024 A* conf
Talaria: Interactively Optimizing Machine Learning Models for Efficient Inference.
CHI
Fred Hohman, Chaoqun Wang, Jinmook Lee, Jochen Görtler, Dominik Moritz, Jeffrey P. Bigham, Zhile Ren, Cecile Foret, Qi Shan, Xiaoyi Zhang
2024 J jnl
Talaria: Interactively Optimizing Machine Learning Models for Efficient Inference.
CoRR
Fred Hohman, Chaoqun Wang, Jinmook Lee, Jochen Görtler, Dominik Moritz, Jeffrey P. Bigham, Zhile Ren, Cecile Foret, Qi Shan, Xiaoyi Zhang
2020 — conf
A 146.52 TOPS/W Deep-Neural-Network Learning Processor with Stochastic Coarse-Fine Pruning and Adaptive Input/Output/Weight Skipping.
VLSI Circuits
Sangyeob Kim, Juhyoung Lee, Sanghoon Kang, Jinmook Lee, Hoi-Jun Yoo
2020 J jnl
The Development of Silicon for AI: Different Design Approaches.
IEEE Trans. Circuits Syst.
Kyuho Jason Lee, Jinmook Lee, Sungpill Choi, Hoi-Jun Yoo
2020 J jnl
The Hardware and Algorithm Co-Design for Energy-Efficient DNN Processor on Edge/Mobile Devices.
IEEE Trans. Circuits Syst.
Jinsu Lee, Sanghoon Kang, Jinmook Lee, Dongjoo Shin, Donghyeon Han, Hoi-Jun Yoo
2019 — conf
A 1.32 TOPS/W Energy Efficient Deep Neural Network Learning Processor with Direct Feedback Alignment based Heterogeneous Core Architecture.
VLSI Circuits
Donghyeon Han, Jinsu Lee, Jinmook Lee, Hoi-Jun Yoo
2019 — conf
A Full HD 60 fps CNN Super Resolution Processor with Selective Caching based Layer Fusion for Mobile Devices.
VLSI Circuits
Juhyoung Lee, Dongjoo Shin, Jinsu Lee, Jinmook Lee, Sanghoon Kang, Hoi-Jun Yoo
2019 J jnl
A Low-Power Deep Neural Network Online Learning Processor for Real-Time Object Tracking Application.
IEEE Trans. Circuits Syst. I Regul. Pap.
Donghyeon Han, Jinsu Lee, Jinmook Lee, Hoi-Jun Yoo
2019 — conf
LNPU: A 25.3TFLOPS/W Sparse Deep-Neural-Network Learning Processor with Fine-Grained Mixed Precision of FP8-FP16.
ISSCC
Jinsu Lee, Juhyoung Lee, Donghyeon Han, Jinmook Lee, Gwangtae Park, Hoi-Jun Yoo
2019 J jnl
UNPU: An Energy-Efficient Deep Neural Network Accelerator With Fully Variable Weight Bit Precision.
IEEE J. Solid State Circuits
Jinmook Lee, Changhyeon Kim, Sanghoon Kang, Dongjoo Shin, Sangyeob Kim, Hoi-Jun Yoo
2018 C conf
A 141.4 mW Low-Power Online Deep Neural Network Training Processor for Real-time Object Tracking in Mobile Devices.
ISCAS
Donghyeon Han, Jinsu Lee, Jinmook Lee, Sungpill Choi, Hoi-Jun Yoo
2018 — conf
B-Face: 0.2 MW CNN-Based Face Recognition Processor with Face Alignment for Mobile User Identification.
VLSI Circuits
Sanghoon Kang, Jinmook Lee, Changhyeon Kim, Hoi-Jun Yoo
2018 J jnl
DNPU: An Energy-Efficient Deep-Learning Processor with Heterogeneous Multi-Core Architecture.
IEEE Micro
Dongjoo Shin, Jinmook Lee, Jinsu Lee, Juhyoung Lee, Hoi-Jun Yoo
2018 J jnl
Low-Power Scalable 3-D Face Frontalization Processor for CNN-Based Face Recognition in Mobile Devices.
IEEE J. Emerg. Sel. Topics Circuits Syst.
Sanghoon Kang, Jinmook Lee, Kyeongryeol Bong, Changhyeon Kim, Youchang Kim, Hoi-Jun Yoo
2018 — conf
UNPU: A 50.6TOPS/W unified deep neural network accelerator with 1b-to-16b fully-variable weight bit-precision.
ISSCC
Jinmook Lee, Changhyeon Kim, Sanghoon Kang, Dongjoo Shin, Sangyeob Kim, Hoi-Jun Yoo
2017 — conf
14.2 DNPU: An 8.1TOPS/W reconfigurable CNN-RNN processor for general-purpose deep neural networks.
ISSCC
Dongjoo Shin, Jinmook Lee, Jinsu Lee, Hoi-Jun Yoo
2017 C conf
A 0.53mW ultra-low-power 3D face frontalization processor for face recognition with human-level accuracy in wearable devices.
ISCAS
Sanghoon Kang, Jinmook Lee, Kyeongryeol Bong, Changhyeon Kim, Hoi-Jun Yoo
2017 — conf
A 21mW low-power recurrent neural network accelerator with quantization tables for embedded deep learning applications.
A-SSCC
Jinmook Lee, Dongjoo Shin, Hoi-Jun Yoo
2017 J jnl
An Energy-Efficient Speech-Extraction Processor for Robust User Speech Recognition in Mobile Head-Mounted Display Systems.
IEEE Trans. Circuits Syst. II Express Briefs
Jinmook Lee, Seongwook Park, Injoon Hong, Hoi-Jun Yoo
2017 — conf
An energy-efficient deep learning processor with heterogeneous multi-core architecture for convolutional neural networks and recurrent neural networks.
COOL Chips
Dongjoo Shin, Jinmook Lee, Jinsu Lee, Juhyoung Lee, Hoi-Jun Yoo
2016 — conf
14.1 A 126.1mW real-time natural UI/UX processor with embedded deep-learning core for low-power smart glasses.
ISSCC
Seongwook Park, Sungpill Choi, Jinmook Lee, Minseo Kim, Junyoung Park, Hoi-Jun Yoo
2016 — conf
An 8.3mW 1.6Msamples/s multi-modal event-driven speech enhancement processor for robust speech recognition in smart glasses.
ESSCIRC
Jinmook Lee, Seongwook Park, Injoon Hong, Hoi-Jun Yoo
2015 — conf
4.6 A1.93TOPS/W scalable deep learning/inference processor with tetra-parallel MIMD architecture for big-data applications.
ISSCC
Seongwook Park, Kyeongryeol Bong, Dongjoo Shin, Jinmook Lee, Sungpill Choi, Hoi-Jun Yoo
2015 C conf
A 3.13nJ/sample energy-efficient speech extraction processor for robust speech recognition in mobile head-mounted display systems.
ISCAS
Jinmook Lee, Seongwook Park, Injoon Hong, Hoi-Jun Yoo
2015 J jnl
An Energy-Efficient and Scalable Deep Learning/Inference Processor With Tetra-Parallel MIMD Architecture for Big Data Applications.
IEEE Trans. Biomed. Circuits Syst.
Seongwook Park, Junyoung Park, Kyeongryeol Bong, Dongjoo Shin, Jinmook Lee, Sungpill Choi, Hoi-Jun Yoo