Showing results 1 to 10 of 10
7.7 LNPU: A 25.3TFLOPS/W Sparse Deep-Neural-Network Learning Processor with Fine-Grained Mixed Precision of FP8-FP16 Lee, Jinsu; Lee, Juhyoung; Han, Donghyeon; Lee, Jinmook; Park, Gwangtae; Yoo, Hoi-Jun, 2019 IEEE International Solid-State Circuits Conference, ISSCC 2019, pp.142 - 144, Institute of Electrical and Electronics Engineers Inc., 2019-02 |
A 0.95 mJ/frame DNN Training Processor for Robust Object Detection with Real-World Environmental Adaptation Han, Donghyeon; Im, DongSeok; Park, Gwangtae; Kim, Youngwoo; Song, Seokchan; Lee, Juhyoung; Yoo, Hoi-Jun, 4th IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022, pp.37 - 40, Institute of Electrical and Electronics Engineers Inc., 2022-06 |
A 49.5 mW Multi-Scale Linear Quantized Online Learning Processor for Real-Time Adaptive Object Detection Song, Seokchan; Yoo, Hoi-Jun; Kim, Soyeon; Park, Gwangtae; Han, Donghyeon, 2022 International Symposium on Circuits and Systems, ISCAS 2022, 2022 International Symposium on Circuits and Systems, 2022-05 |
A DNN Training Processor for Robust Object Detection with Real-World Environmental Adaptation Han, Donghyeon; Im, DongSeok; Park, Gwangtae; Kim, Youngwoo; Song, Seokchan; Lee, Juhyoung; Yoo, Hoi-Jun, 4th IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2022, pp.501, Institute of Electrical and Electronics Engineers Inc., 2022-06 |
A Low-power and Real-time 3D Object Recognition Processor with Dense RGB-D Data Acquisition in Mobile Platforms Im, Dongseok; Park, Gwangtae; Ryu, Junha; Li, Zhiyong; Kang, Sanghoon; Han, Donghyeon; Lee, Jinsu; et al, 25th IEEE Symposium in Low-Power and High-Speed Chips (COOL CHIPS), IEEE, 2022-04 |
An Energy-efficient Deep Neural Network Training Processor with Bit-slice-level Reconfigurability and Sparsity Exploitation Han, Donghyeon; Im, Dongseok; Park, Gwangtae; Kim, Youngwoo; Song, Seokchan; Lee, Juhyoung; Yoo, Hoi-Jun, IEEE Symposium on Low-Power and High-Speed Chips (IEEE COOL CHIPS), IEEE COMPUTER SOC, 2021-04 |
DSPU: A 281.6mW Real-Time Deep Learning-Based Dense RGB-D Data Acquisition with Sensor Fusion and 3D Perception System-on-Chip Im, DongSeok; Park, Gwangtae; Li, Zhiyong; Ryu, Junha; Kang, Sanghoon; Han, Donghyeon; Lee, Jinsu; et al, 2022 IEEE Hot Chips 34 Symposium, HCS 2022, Institute of Electrical and Electronics Engineers Inc., 2022-08 |
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 Im, DongSeok; Park, Gwangtae; LI, ZHIYONG; Ryu, Junha; Kang, Sanghoon; Han, Donghyeon; Lee, Jinsu; et al, 2022 IEEE International Solid-State Circuits Conference, ISSCC 2022, pp.510 - 512, Institute of Electrical and Electronics Engineers Inc., 2022-02 |
HNPU-V2: A 46.6 FPS DNN Training Processor for Real-World Environmental Adaptation based Robust Object Detection on Mobile Devices Han, Donghyeon; Im, DongSeok; Park, Gwangtae; Kim, Youngwoo; Song, Seokchan; Lee, Juhyoung; Yoo, Hoi-Jun, 2022 IEEE Hot Chips 34 Symposium, HCS 2022, Institute of Electrical and Electronics Engineers Inc., 2022-08 |
LNPU: An Energy-Efficient Deep-Neural-Network Training Processor with Fine-Grained Mixed Precision Lee, Jinsu; Lee, Juhyoung; Han, Donghyeon; Lee, Jinmook; Park, Gwangtae; Yoo, Hoi-Jun, Hot Chips 2019: A Symposium on High-Performance Chips, HOT CHIPS, 2019-08 |
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