Research Interest

- Wearable Healthcare SoC

- Biomedical Sensor Front-end

Education

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

BIOGRAPHY

- 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

Research Interest

- DNN Accelerator Architecture

- DNN SW/HW Co-Design

- Mobile DNN Optimization

Education

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

Awards

BIOGRAPHY

- International Journal Papers

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

- International Conference Papers

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

Symposium on VLSI Circuits (SoVC), Jun. 2018

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

Research Interest

- Low-power SoC Design

- Deep Learning & AI Algorithm

- Computer Vision & HRI System

Award

Education

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

BIOGRAPHY

- International Journal P​apers

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 

Donghyeon Han, Dongseok Im, Gwangtae Park, Youngwoo Kim, Seokchan Song, Juhyoung Lee, and Hoi-Jun Yoo

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

- International Conference Papers

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

Donghyeon Han, Dongseok Im, Gwangtae Park, Youngwoo Kim, Seokchan Song, Juhyoung Lee, and Hoi-Jun Yoo

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

Donghyeon Han, Dongseok Im, Gwangtae Park, Youngwoo Kim, Seokchan Song, Juhyoung Lee, and Hoi-Jun Yoo

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 

Donghyeon Han, Dongseok Im, Gwangtae Park, Youngwoo Kim, Seokchan Song, Juhyoung Lee, and Hoi-Jun Yoo

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

Research Interest

- Machine learning based SoC Design

Education

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      Daegu Il Science High School

BIOGRAPHY

- International Journal P​apers

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

- International Conference Papers

Low-power Autonomous Adaptation System with Deep Reinforcement Learning

Juhyoung Lee, Wooyoung Jo, Seong-Wook Park, and Hoi-Jun Yoo

IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Jun. 2022

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

IEEE Symposium on High Performance Chips (HOT Chips), Aug. 2021

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

IEEE Symposium on VLSI Circuits (S. VLSI), Jun. 2021

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

IEEE Symposium on VLSI Circuits (S. VLSI), Jun. 2019

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

IEEE International Symposium on Circuit and Systems (ISCAS), May. 2018