Juhyoung Lee

发表

Low-power Autonomous Adaptation System with Deep Reinforcement Learning

H. Yoo, Seong-Wook Park, Juhyoung Lee, 2022, 2022 IEEE 4th International Conference on Artificial Intelligence Circuits and Systems (AICAS).

Energy-Efficient DNN Training Processors on Micro-AI Systems

H. Yoo, Donghyeon Han, Sangyeob Kim, 2022, IEEE Open Journal of the Solid-State Circuits Society.

An energy-efficient deep learning processor with heterogeneous multi-core architecture for convolutional neural networks and recurrent neural networks

Hoi-Jun Yoo, Dongjoo Shin, Jinmook Lee, 2017, 2017 IEEE Symposium in Low-Power and High-Speed Chips (COOL CHIPS).

An Energy-Efficient Sparse Deep-Neural-Network Learning Accelerator With Fine-Grained Mixed Precision of FP8–FP16

Hoi-Jun Yoo, Jinmook Lee, Jinsu Lee, 2019, IEEE Solid-State Circuits Letters.

7.4 GANPU: A 135TFLOPS/W Multi-DNN Training Processor for GANs with Speculative Dual-Sparsity Exploitation

Hoi-Jun Yoo, Donghyeon Han, Soyeon Kim, 2020, 2020 IEEE International Solid- State Circuits Conference - (ISSCC).

7.7 LNPU: A 25.3TFLOPS/W Sparse Deep-Neural-Network Learning Processor with Fine-Grained Mixed Precision of FP8-FP16

Hoi-Jun Yoo, Jinmook Lee, Donghyeon Han, 2019, 2019 IEEE International Solid- State Circuits Conference - (ISSCC).

DNPU: An Energy-Efficient Deep-Learning Processor with Heterogeneous Multi-Core Architecture

Hoi-Jun Yoo, Dongjoo Shin, Jinmook Lee, 2018, IEEE Micro.

A 13.7 TFLOPS/W Floating-point DNN Processor using Heterogeneous Computing Architecture with Exponent-Computing-in-Memory

Hoi-Jun Yoo, Wooyoung Jo, Juhyoung Lee, 2021, 2021 Symposium on VLSI Circuits.

A 1.02-μW STT-MRAM-Based DNN ECG Arrhythmia Monitoring SoC With Leakage-Based Delay MAC Unit

Hongsik Jeong, Hoi-Jun Yoo, Changhyeon Kim, 2020, IEEE Solid-State Circuits Letters.

TSUNAMI: Triple Sparsity-Aware Ultra Energy-Efficient Neural Network Training Accelerator With Multi-Modal Iterative Pruning

H. Yoo, Donghyeon Han, Sangyeob Kim, 2022, IEEE Transactions on Circuits and Systems I: Regular Papers.

A 0.95 mJ/frame DNN Training Processor for Robust Object Detection with Real-World Environmental Adaptation

H. Yoo, Donghyeon Han, Youngwoo Kim, 2022, International Conference on Artificial Intelligence Circuits and Systems.

A 146.52 TOPS/W Deep-Neural-Network Learning Processor with Stochastic Coarse-Fine Pruning and Adaptive Input/Output/Weight Skipping

Hoi-Jun Yoo, Jinmook Lee, Sanghoon Kang, 2020, 2020 IEEE Symposium on VLSI Circuits.

PNPU: An Energy-Efficient Deep-Neural-Network Learning Processor With Stochastic Coarse–Fine Level Weight Pruning and Adaptive Input/Output/Weight Zero Skipping

Hoi-Jun Yoo, Jinmook Lee, Juhyoung Lee, 2021, IEEE Solid-State Circuits Letters.

HNPU-V2: A 46.6 FPS DNN Training Processor for Real-World Environmental Adaptation based Robust Object Detection on Mobile Devices

H. Yoo, Donghyeon Han, Juhyoung Lee, 2022, 2022 IEEE Hot Chips 34 Symposium (HCS).

OmniDRL: A 29.3 TFLOPS/W Deep Reinforcement Learning Processor with Dualmode Weight Compression and On-chip Sparse Weight Transposer

Hoi-Jun Yoo, Wooyoung Jo, Juhyoung Lee, 2021, 2021 Symposium on VLSI Circuits.

SNPU: Always-on 63.2μW Face Recognition Spike Domain Convolutional Neural Network Processor with Spike Train Decomposition and Shift-and-Accumulation Unit

H. Yoo, Sangyeob Kim, Juhyoung Lee, 2022, International Symposium on Security in Computing and Communications.

A Power-Efficient CNN Accelerator With Similar Feature Skipping for Face Recognition in Mobile Devices

Hoi-Jun Yoo, Jinsu Lee, Juhyoung Lee, 2020, IEEE Transactions on Circuits and Systems I: Regular Papers.

Z-PIM: A Sparsity-Aware Processing-in-Memory Architecture With Fully Variable Weight Bit-Precision for Energy-Efficient Deep Neural Networks

Juhyoung Lee, Jinsu Lee, Ji-Hoon Kim, 2021, IEEE Journal of Solid-State Circuits.

A Full HD 60 fps CNN Super Resolution Processor with Selective Caching based Layer Fusion for Mobile Devices

Hoi-Jun Yoo, Dongjoo Shin, Jinmook Lee, 2019, 2019 Symposium on VLSI Circuits.

SNPU: An Energy-Efficient Spike Domain Deep-Neural-Network Processor With Two-Step Spike Encoding and Shift-and-Accumulation Unit

Sangyeob Kim, Juhyoung Lee, Soyeon Kim, 2023, IEEE Journal of Solid-State Circuits.

SRNPU: An Energy-Efficient CNN-Based Super-Resolution Processor With Tile-Based Selective Super-Resolution in Mobile Devices

Hoi-Jun Yoo, Jinsu Lee, Juhyoung Lee, 2020, IEEE Journal on Emerging and Selected Topics in Circuits and Systems.

GST: Group-Sparse Training for Accelerating Deep Reinforcement Learning

Hoi-Jun Yoo, Sangjin Kim, Juhyoung Lee, 2021, ArXiv.

OmniDRL: An Energy-Efficient Deep Reinforcement Learning Processor With Dual-Mode Weight Compression and Sparse Weight Transposer

H. Yoo, Donghyeon Han, Sangyeob Kim, 2022, IEEE Journal of Solid-State Circuits.

A 54.7 fps 3D Point Cloud Semantic Segmentation Processor with Sparse Grouping Based Dilated Graph Convolutional Network for Mobile Devices

Hoi-Jun Yoo, Sangjin Kim, Juhyoung Lee, 2020, 2020 IEEE International Symposium on Circuits and Systems (ISCAS).