LNPU: A 25.3TFLOPS/W Sparse Deep-Neural-Network Learning Processor with Fine-Grained Mixed Precision of FP8-FP16. - Jinsu Lee; Juhyoung Lee; Donghyeon Han; Jinmook Lee; Gwangtae Park; Hoi-Jun Yoo (ISSCC, 2019)

Extension of Direct Feedback Alignment to Convolutional and Recurrent Neural Network for Bio-plausible Deep Learning. - Donghyeon Han; Gwangtae Park; Junha Ryu; Hoi-Jun Yoo (CoRR, 2020)

A 1.15 TOPS/W Energy-Efficient Capsule Network Accelerator for Real-Time 3D Point Cloud Segmentation in Mobile Environment. - Gwangtae Park; Dongseok Im; Donghyeon Han; Hoi-Jun Yoo (IEEE Trans. Circuits Syst. II Express Briefs, 2020)

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; Hoi-Jun Yoo (COOL CHIPS, 2021)

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; Hoi-Jun Yoo (IEEE J. Emerg. Sel. Topics Circuits Syst., 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; Hoi-Jun Yoo (IEEE J. Solid State Circuits, 2021)

A 0.82 μW CIS-Based Action Recognition SoC With Self-Adjustable Frame Resolution for Always-on IoT Devices. - Junha Ryu; Gwangtae Park; Dongseok Im; Ji-Hoon Kim 004; Hoi-Jun Yoo (IEEE Trans. Circuits Syst. II Express Briefs, 2021)

A 0.95 mJ/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; Hoi-Jun Yoo (AICAS, 2022)

A DNN Training Processor for Robust Object Detection with Real-World Environmental Adaptation. - Donghyeon Han; Dongseok Im; Gwangtae Park; Youngwoo Kim; Seokchan Song; Juhyoung Lee; Hoi-Jun Yoo (AICAS, 2022)

A Low-power and Real-time 3D Object Recognition Processor with Dense RGB-D Data Acquisition in Mobile Platforms. - Dongseok Im; Gwangtae Park; Junha Ryu; Zhiyong Li 016; Sanghoon Kang; Donghyeon Han; Jinsu Lee; Wonhoon Park; Hankyul Kwon; Hoi-Jun Yoo (COOL CHIPS, 2022)

HNPU-V2: A 46.6 FPS DNN Training Processor for Real-World Environmental Adaptation based Robust Object Detection on Mobile Devices. - Donghyeon Han; Dongseok Im; Gwangtae Park; Youngwoo Kim; Seokchan Song; Juhyoung Lee; Hoi-Jun Yoo (HCS, 2022)

DSPU: A 281.6mW Real-Time Deep Learning-Based Dense RGB-D Data Acquisition with Sensor Fusion and 3D Perception System-on-Chip. - Dongseok Im; Gwangtae Park; Zhiyong Li 016; Junha Ryu; Sanghoon Kang; Donghyeon Han; Jinsu Lee; Wonhoon Park; Hankyul Kwon; Hoi-Jun Yoo (HCS, 2022)

DSPU: A 281.6mW Real-Time Depth Signal Processing Unit for Deep Learning-Based Dense RGB-D Data Acquisition with Depth Fusion and 3D Bounding Box Extraction in Mobile Platforms. - Dongseok Im; Gwangtae Park; Zhiyong Li 016; Junha Ryu; Sanghoon Kang; Donghyeon Han; Jinsu Lee; Hoi-Jun Yoo (ISSCC, 2022)

Energy-efficient Dense DNN Acceleration with Signed Bit-slice Architecture. - Dongseok Im; Gwangtae Park; Zhiyong Li 016; Junha Ryu; Hoi-Jun Yoo (CoRR, 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; Hoi-Jun Yoo (IEEE Micro, 2022)

A 49.5 mW Multi-Scale Linear Quantized Online Learning Processor for Real-Time Adaptive Object Detection. - Seokchan Song; Soyeon Kim; Gwangtae Park; Donghyeon Han; Hoi-Jun Yoo (IEEE Trans. Circuits Syst. II Express Briefs, 2022)

A 33.6 FPS Embedding based Real-time Neural Rendering Accelerator with Switchable Computation Skipping Architecture on Edge Device. - Jongjun Park; Donghyeon Han; Junha Ryu; Dongseok Im; Gwangtae Park; Hoi-Jun Yoo (A-SSCC, 2023)

Sibia: Signed Bit-slice Architecture for Dense DNN Acceleration with Slice-level Sparsity Exploitation. - Dongseok Im; Gwangtae Park; Zhiyong Li 016; Junha Ryu; Hoi-Jun Yoo (HPCA, 2023)

A 15.9 mW 96.5 fps Memory-Efficient 3D Reconstruction Processor with Dilation-based TSDF Fusion and Block-Projection Cache System. - Hankyul Kwon; Gwangtae Park; Junha Ryu; Wooyoung Jo; Hoi-Jun Yoo (ISCAS, 2023)

GPPU: A 330.4-μJ/ task Neural Path Planning Processor with Hybrid GNN Acceleration for Autonomous 3D Navigation. - Seokchan Song; Donghyeon Han; Sangjin Kim; Sangyeob Kim; Gwangtae Park; Hoi-Jun Yoo (VLSI Technology and Circuits, 2023)

DSPU: An Efficient Deep Learning-Based Dense RGB-D Data Acquisition With Sensor Fusion and 3-D Perception SoC. - Dongseok Im; Gwangtae Park; Junha Ryu; Zhiyong Li 016; Sanghoon Kang; Donghyeon Han; Jinsu Lee; Wonhoon Park; Hankyul Kwon; Hoi-Jun Yoo (IEEE J. Solid State Circuits, 2023)

A Mobile 3-D Object Recognition Processor With Deep-Learning-Based Monocular Depth Estimation. - Dongseok Im; Gwangtae Park; Zhiyong Li 016; Junha Ryu; Sanghoon Kang; Donghyeon Han; Jinsu Lee; Wonhoon Park; Hankyul Kwon; Hoi-Jun Yoo (IEEE Micro, 2023)

A 66.6 FPS High Quality Gaussian Splats Rendering FPGA Processor with Reconfigurable Computation Architecture. - Hongseok Lee; Gwangtae Park; Wonhoon Park; Wooyoung Jo; Jongjun Park; Hoi-Jun Yoo (A-SSCC, 2024)

A Low-power and Real-time Neural-Rendering Dense SLAM Processor with 3-Level Hierarchical Sparsity Exploitation. - Gwangtae Park; Seokchan Song; Haoyang Sang; Dongseok Im; Donghyeon Han; Sangyeob Kim; Hongseok Lee; Hoi-Jun Yoo (COOL CHIPS, 2024)

20.8 Space-Mate: A 303.5mW Real-Time Sparse Mixture-of-Experts-Based NeRF-SLAM Processor for Mobile Spatial Computing. - Gwangtae Park; Seokchan Song; Haoyang Sang; Dongseok Im; Donghyeon Han; Sangyeob Kim; Hongseok Lee; Hoi-Jun Yoo (ISSCC, 2024)

NeRF-Navi: A 93.6-202.9µJ/task Switchable Approximate-Accurate NeRF Path Planning Processor with Dual Attention Engine and Outlier Bit-Offloading Core. - Seryeong Kim; Seokchan Song; Wonhoon Park; Junha Ryu; Sangyeob Kim; Gwangtae Park; Soyeon Kim; Hoi-Jun Yoo (VLSI Technology and Circuits, 2024)

A 8.81 TFLOPS/W Deep-Reinforcement-Learning Accelerator With Delta-Based Weight Sharing and Block-Mantissa Reconfigurable PE Array. - Sanghyuk An; Junha Ryu; Gwangtae Park; Hoi-Jun Yoo (IEEE Trans. Circuits Syst. II Express Briefs, 2024)

An 2.31uJ/Inference Ultra-Low Power Always-on Event-Driven AI-IoT SoC With Switchable nvSRAM Compute-in-Memory Macro. - Haoyang Sang; Wenao Xie; Gwangtae Park; Hoi-Jun Yoo (IEEE Trans. Circuits Syst. II Express Briefs, 2024)

23.3 EdgeDiff: 418.4mJ/Inference Multi-Modal Few-Step Diffusion Model Accelerator with Mixed-Precision and Reordered Group Quantization. - Sangjin Kim; Jungjun Oh; Jeonggyu So; Yuseon Choi; Sangyeob Kim; Dongseok Im; Gwangtae Park; Hoi-Jun Yoo (ISSCC, 2025)

IRIS: A 8.55mJ/frame Spatial Computing SoC for Interactable Rendering and Surface-Aware Modeling with 3D Gaussian Splatting. - Seokchan Song; Seryeong Kim; Wonhoon Park; Jongjun Park; Sanghyuk An; Gwangtae Park; Minseo Kim; Hoi-Jun Yoo (ISSCC, 2025)