- Wearable Healthcare SoC
- Biomedical Sensor Front-end
2021. 2 Ph.D. Student in EE, Korea Advanced Institute of Science and Technology (KAIST)
2016. 8 M.S. in EE, Korea Advanced Institute of Science and Technology (KAIST)
2014. 8 B.S. in EE, Korea Advanced Insititue of Science and Technology (KAIST)
2010. 2 Korea Science Academy
- International Journal Papers
A 9.6-mW/Ch 10-MHz Wide-Bandwidth Electrical Impedance Tomography IC With Accurate Phase Compensation for Early Breast Cancer Detection
Jaehyuk Lee, Surin Gweon, Kwonjoon Lee, Soyeon Um, Kyoung-Rog Lee, and Hoi-Jun Yoo
IEEE Journal of Solid-State Circuits (JSSC), Nov. 2020
A 0.8-V 82.9-μW In-Ear BCI Controller IC With 8.8 PEF EEG Instrumentation Amplifier and Wireless BAN Transceiver
Jaehyuk Lee, Kyoung-Rog Lee, Unsoo Ha, Ji-Hoon Kim, Kwonjoon Lee, Surin Gweon, Jaeeun Jang, and Hoi-Jun Yoo
IEEE Journal of Solid-State Circuits (JSSC), Apr. 2018
0.025mJ/Image Fast-scan and SNR Enhanced Electrical Impedance Tomography IC for Lung Ventilation Monitoring
Jaehyuk Lee, Unsoo Ha and Hoi-Jun Yoo
Journal of Semiconductor Science and Technology (JSTS)
- International Conference Papers
A 9.6 mW/Ch 10 MHz Wide-bandwidth Electrical Impedance Tomography IC with Accurate Phase Compensation for Breast Cancer Detection
Jaehyuk Lee, Surin Gweon, Kwonjoon Lee, Soyeon Um, Kyoung-Rog Lee, Kwantae Kim, Jihee Lee, and Hoi-Jun Yoo
Custom Integrated Circuits Conference (CICC), March, 2020
A 0.8V 82.9μW In-ear BCI Controller System with 8.8 PEF EEG Instrumentational Amplifier and Wireless BAN Transceiver
Jaehyuk Lee, Kyoung-Rog Lee, Unsoo Ha, Ji-Hoon Kim, Kwonjoon Lee, and Hoi-jun Yoo
Symposium on VLSI Circuits (SoVC), Jun. 2018
30-fps SNR Equalized Electrical Impedance Tomography IC with Fast-Settle Filter and Adaptive Current Control for Lung Monitoring
Jaehyuk Lee, Unsoo Ha, and Hoi-Jun Yoo
IEEE International Symposium on Circuit and Systems (ISCAS), May 2016
- DNN Accelerator Architecture
- DNN SW/HW Co-Design
- Mobile DNN Optimization
2022. 2 Ph.D. in EE, Korea Advanced Institute of Science and Technology
2018. 2 M.S. in EE, Korea Advanced Institute of Science and Technology
2016. 2 B.S. in EE, Korea Advanced Insititue of Science and Technology
2012. 2 Goyang Foreign Language High School
An Overview of Sparsity Exploitation in CNNs for On-Device Intelligence with Software-Hardware Cross-Layer Optimizations
Sanghoon Kang, Gwangtae Park, Sangjin Kim, Soyeon Kim, Donghyeon Han and Hoi-Jun Yoo
IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS), Oct. 2021
GANPU: An Energy-Efficient Multi-DNN Training Processor for GANs with Speculative Dual-Sparsity Exploitation
Sanghoon Kang, Donghyeon Han, Juhyoung Lee, Dongseok Im, Sangyeob Kim, Soyeon Kim, Junha Ryu and Hoi-Jun Yoo
IEEE Journal of Solid-State Circuits, 2021
Low-Power Scalable 3-D Face Frontalization Processor for CNN-based Face Recognition in Mobile Devices
Sanghoon Kang,Jinmook Lee, Kyeongryeol Bong, Changhyeon Kim, Youchang Kim, and Hoi-Jun Yoo
IEEE Journal on Emerging and Selected Topics in Circuits and Systems, Jun. 2018
GANPU: A Versatile Many-Core Processor for Training GAN on Mobile Devices with Speculative Dual-Sparsity Exploitation
Sanghoon Kang, Donghyeon Han, Juhyoung Lee, Dongseok Im, Sangyeob Kim, Soyeon Kim, Junha Ryu, and Hoi-Jun Yoo
IEEE Hot Chips: A Symposium on High Performance Chips, Aug. 2020
GANPU: A 135TFLOPS/W Multi-DNN Training Processor for GANs with Speculative Dual Sparsity Exploitation
Sanghoon Kang, Donghyeon Han, Juhyoung Lee, Dongseok Im, Sangyeob Kim, Soyeon Kim, and Hoi-Jun Yoo
International Solid-State Circuits Conference (ISSCC), Feb. 2020
B-Face: 0.2 mW CNN-Based Face Recognition Processor with Face Alignment for Mobile User Identification
Sanghoon Kang, Jinmook Lee, Changhyeon Kim, and Hoi-jun Yoo
A 0.53mW Ultra-Low-Power 3D Face Frontalization Processor for Face Recognition with Human-Level Accuracy in Wearable Devices
Sanghoon Kang, Jinmook Lee, Kyeongryeol Bong, Changhyeon Kim, Hoi-Jun Yoo
IEEE International Symposium on Circuit and Systems (ISCAS), May. 2017
- Low-power SoC Design
- Deep Learning & AI Algorithm
- Computer Vision & HRI System
2019. 3 ~ Ph.D. Student in EE, Korea Advanced Institute of Science and Technology (KAIST)
2019. 2 M.S. in EE, Korea Advanced Institute of Science and Technology (KAIST)
2017. 2 B.S. in EE, Korea Advanced Insititue of Science and Technology (KAIST)
2013. 2 Daejeon Science High School
- International Journal Papers
Energy-efficient DNN Training Processor on Micro-AI Systems
Donghyeon Han, Sanghoon Kang, Sangyeob Kim, Juhyoung Lee, and Hoi-Jun Yoo
IEEE Open Journal of the Solid-State Circuits Society (OJSSCS), 2022
A Mobile DNN Training Processor with Automatic Bit-precision Search and Fine-grained Sparsity Exploitation
Donghyeon Han, Dongseok Im, Gwangtae Park, Youngwoo Kim, Seokchan Song, Juhyoung Lee, and Hoi-Jun Yoo
IEEE Micro, Dec. 2021
HNPU: An Adaptive DNN Training Processor Utilizing Stochastic Dynamic Fixed-point and Active Bit-precision Searching
IEEE Journal of Solid-State Circuits (JSSC), Sep. 2021
DF-LNPU: A Pipelined Direct Feedback Alignment-Based Deep Neural Network Learning Processor for Fast Online Learning
Donghyeon Han, Jinsu Lee, and Hoi-jun Yoo
IEEE Journal of Solid-State Circuits (JSSC), Dec. 2020
A Low-Power Deep Neural Network Online Learning Processor for Real-Time Object Tracking Application
Donghyeon Han, Jinsu Lee, Jinmook Lee, and Hoi-jun Yoo
IEEE Transactions on Circuits and Systems I (TCAS-I), Nov. 2018
A Low-power Neural 3D Rendering Processor with Bio-inspired Visual Perception Core and Hybrid DNN Acceleration
Donghyeon Han, Junha Ryu, Sangyeob Kim, Sangjin Kim, Jongjun Park and Hoi-Jun Yoo
IEEE Symposium on Low-Power and High-Speed Chips (COOL Chips), Mar. 2023
MetaVRain: A 133mW Real-time Hyper-realistic-3D-NeRF Processor with 1D-2D Hybrid-Neural-Engines for Metaverse on Mobile Devices
Donghyeon Han, Junha Ryu, Sangyeob Kim, Sangjin Kim, and Hoi-Jun Yoo
IEEE International Conference on Solid-State Circuits (ISSCC), Feb. 2023
HNPU-V2: A 46.6 FPS DNN Training Processor for Real-World Environmental Adaptation based Robust Object Detection on Moble Devices
IEEE Symposium on High Performance Chips (HOT Chips), Aug. 2022
A 0.95mJ/frame DNN Training Processor for Robust Object Detection with Real-World Environmental Adaptation
IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Jun. 2022
An Energy-efficient Deep Neural Network Training Processor with Bit-slice-level Reconfigurability and Sparsity Exploitation
IEEE Symposium on Low-Power and High-Speed Chips (COOL Chips), Apr. 2021
Direct Feedback Alignment based Convolutional Neural Network Training for Low-power Online Learning Processor
Donghyeon Han, and Hoi-Jun Yoo
IEEE International Conference on Computer Vision (ICCVW), Nov. 2019
A 1.32 TOPS/W Energy Efficient Deep Neural Network Learning Processor with Direct Feedback Alignment based Heterogeneous Core Architecture
Donghyeon Han, Jinsu Lee, Jinmook Lee, and Hoi-Jun Yoo
IEEE Symposium on VLSI Circuits (S. VLSI), Jun. 2019
A 141.4 mW Low-Power Online Deep Neural Network Training Processor for Real-time Object Tracking in Mobile Devices
Donghyeon Han, Jinsu Lee, Jinmook Lee, Sungpill Choi, and Hoi-jun Yoo
IEEE International Symposium on Circuit and Systems (ISCAS), May. 2018
- Machine learning based SoC Design
2013. 2 Daegu Il Science High School
OmniDRL: An Energy-Efficient Deep Reinforcement Learning Processor with Dual-mode Weight Compression and Sparse Weight Transposer
Juhyoung Lee, Sangyeob Kim, Sangjin Kim, Wooyoung Jo, Donghyeon Han, and Hoi-Jun Yoo
IEEE Journal of Solid-State Circuits (JSSC), Jan. 2022
ECIM: Exponent Computing in Memory for an Energy Efficient Heterogeneous Floating-Point DNN Training Processor
Juhyoung Lee, Jihoon Kim, Wooyoung Jo, Sangyeob Kim, Sangjin Kim, and Hoi-Jun Yoo
IEEE Micro, Jul. 2021
SRNPU: An Energy-Efficient CNN-Based Super-Resolution Processor With Tile-Based Selective Super-Resolution in Mobile Devices
Juhyoung Lee, Jinsu Lee, and Hoi-Jun Yoo
IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS), Aug. 2020
Low-power Autonomous Adaptation System with Deep Reinforcement Learning
Juhyoung Lee, Wooyoung Jo, Seong-Wook Park, and Hoi-Jun Yoo
OmniDRL: An Energy-Efficient Mobile Deep Reinforcement Learning Accelerators with Dual-mode Weight Compression and Direct Processing of Compressed Data
Juhyoung Lee, Sangyeob Kim, Jihoon Kim, Sangjin Kim, Wooyoung Jo, Donghyeon Han, and Hoi-Jun Yoo
IEEE Symposium on High Performance Chips (HOT Chips), Aug. 2021
An Energy-efficient Floating-Point DNN Processor using Heterogeneous Computing Architecture with Exponent-Computing-in-Memory
Juhyoung Lee, Jihoon Kim, Wooyoung Jo, Sangyeob Kim, Sangjin Kim, Donghyeon Han, Jinsu Lee, and Hoi-Jun Yoo
Energy-Efficient Deep Reinforcement Learning Accelerator Designs for Mobile Autonomous Systems
Juhyoung Lee, Changhyeon Kim, Donghyeon Han, Sangyeob Kim, Sangjin Kim, and Hoi-Jun Yoo
IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Jun. 2021
A 13.7 TFLOPS/W Floating-point DNN Processor using Heterogeneous Computing Architecture with Exponent-Computing-in-Memory
Juhyoung Lee, Jihoon Kim, Wooyoung Jo, Sangyeob Kim, Sangjin Kim, Jinsu Lee, and Hoi-Jun Yoo
IEEE Symposium on VLSI Circuits (S. VLSI), Jun. 2021
OmniDRL: A 29.3 TFLOPS/W Deep Reinforcement Learning Processor with Dual-mode Weight Compression and On-chip Sparse Weight Transposer
Juhyoung Lee, Sangyeob Kim, Sangjin Kim, Wooyoung Jo, Donghyeon Han, Jinsu Lee, and Hoi-Jun Yoo
A Full HD 60 fps CNN Super Resolution Processor with Selective Caching based Layer Fusion for Mobile Devices
Juhyoung Lee, Dongjoo Shin, Jinsu Lee, Jinmook Lee, Sanghoon Kang, and Hoi-Jun Yoo
A 46.1 fps Global Matching Optical Flow Estimation Processor for Action Recognition in Mobile Devices
Juhyoung Lee, Changhyeon Kim, Sungpill Choi, Dongjoo Shin, Sanghoon Kang, and Hoi-jun Yoo
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