Gwangtae Park
Orcid: 0000-0002-9132-0311
According to our database1,
Gwangtae Park
authored at least 23 papers
between 2019 and 2024.
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Bibliography
2024
20.8 Space-Mate: A 303.5mW Real-Time Sparse Mixture-of-Experts-Based NeRF-SLAM Processor for Mobile Spatial Computing.
Proceedings of the IEEE International Solid-State Circuits Conference, 2024
2023
A Mobile 3-D Object Recognition Processor With Deep-Learning-Based Monocular Depth Estimation.
IEEE Micro, 2023
DSPU: An Efficient Deep Learning-Based Dense RGB-D Data Acquisition With Sensor Fusion and 3-D Perception SoC.
IEEE J. Solid State Circuits, 2023
GPPU: A 330.4-μJ/ task Neural Path Planning Processor with Hybrid GNN Acceleration for Autonomous 3D Navigation.
Proceedings of the 2023 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits), 2023
A 15.9 mW 96.5 fps Memory-Efficient 3D Reconstruction Processor with Dilation-based TSDF Fusion and Block-Projection Cache System.
Proceedings of the IEEE International Symposium on Circuits and Systems, 2023
Sibia: Signed Bit-slice Architecture for Dense DNN Acceleration with Slice-level Sparsity Exploitation.
Proceedings of the IEEE International Symposium on High-Performance Computer Architecture, 2023
A 33.6 FPS Embedding based Real-time Neural Rendering Accelerator with Switchable Computation Skipping Architecture on Edge Device.
Proceedings of the IEEE Asian Solid-State Circuits Conference, 2023
2022
A 49.5 mW Multi-Scale Linear Quantized Online Learning Processor for Real-Time Adaptive Object Detection.
IEEE Trans. Circuits Syst. II Express Briefs, 2022
A Mobile DNN Training Processor With Automatic Bit Precision Search and Fine-Grained Sparsity Exploitation.
IEEE Micro, 2022
CoRR, 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.
Proceedings of the IEEE International Solid-State Circuits Conference, 2022
DSPU: A 281.6mW Real-Time Deep Learning-Based Dense RGB-D Data Acquisition with Sensor Fusion and 3D Perception System-on-Chip.
Proceedings of the 2022 IEEE Hot Chips 34 Symposium, 2022
HNPU-V2: A 46.6 FPS DNN Training Processor for Real-World Environmental Adaptation based Robust Object Detection on Mobile Devices.
Proceedings of the 2022 IEEE Hot Chips 34 Symposium, 2022
A Low-power and Real-time 3D Object Recognition Processor with Dense RGB-D Data Acquisition in Mobile Platforms.
Proceedings of the IEEE Symposium in Low-Power and High-Speed Chips, 2022
A DNN Training Processor for Robust Object Detection with Real-World Environmental Adaptation.
Proceedings of the 4th IEEE International Conference on Artificial Intelligence Circuits and Systems, 2022
A 0.95 mJ/frame DNN Training Processor for Robust Object Detection with Real-World Environmental Adaptation.
Proceedings of the 4th IEEE International Conference on Artificial Intelligence Circuits and Systems, 2022
2021
A 0.82 μW CIS-Based Action Recognition SoC With Self-Adjustable Frame Resolution for Always-on IoT Devices.
IEEE Trans. Circuits Syst. II Express Briefs, 2021
HNPU: An Adaptive DNN Training Processor Utilizing Stochastic Dynamic Fixed-Point and Active Bit-Precision Searching.
IEEE J. Solid State Circuits, 2021
An Overview of Sparsity Exploitation in CNNs for On-Device Intelligence With Software-Hardware Cross-Layer Optimizations.
IEEE J. Emerg. Sel. Topics Circuits Syst., 2021
An Energy-Efficient Deep Neural Network Training Processor with Bit-Slice-Level Reconfigurability and Sparsity Exploitation.
Proceedings of the IEEE Symposium in Low-Power and High-Speed Chips, 2021
2020
A 1.15 TOPS/W Energy-Efficient Capsule Network Accelerator for Real-Time 3D Point Cloud Segmentation in Mobile Environment.
IEEE Trans. Circuits Syst. II Express Briefs, 2020
Extension of Direct Feedback Alignment to Convolutional and Recurrent Neural Network for Bio-plausible Deep Learning.
CoRR, 2020
2019
LNPU: A 25.3TFLOPS/W Sparse Deep-Neural-Network Learning Processor with Fine-Grained Mixed Precision of FP8-FP16.
Proceedings of the IEEE International Solid- State Circuits Conference, 2019