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Showing results 13 to 19 of 19

13
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

14
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

15
DT-CNN: Dilated and transposed convolution neural network accelerator for real-time image segmentation on mobile devices

Im, Dongseok; Han, Donghyeon; Choi, Sungpill; Kang, Sanghoon; Yoo, Hoi-Jun, 2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019, Institute of Electrical and Electronics Engineers Inc., 2019-05

16
FlashMAC: An Energy-Efficient Analog-Digital Hybrid MAC with Variable Latency-Aware Scheduling

Gweon, Surin; Kang, Sanghoon; Han, Donghyeon; Lee, Kyoung-Rog; Kim, Kwantae; Yoo, Hoi-Jun, 2021 IEEE Asian Solid-State Circuits Conference (A-SSCC), IEEE, 2021-11-07

17
GANPU: A 135TFLOPS/W Multi-DNN Training Processor for GANs with Speculative Dual-Sparsity Exploitation

Kang, Sanghoon; Yoo, Hoi-Jun; Han, Donghyeon; LEE, JUHYOUNG; lM,DONGSEOK; Kim, Sangyeob; Kim, Soyeon, IEEE International Solid-State Circuits Conference, ISSCC 2020, pp.140 - 142, Institute of Electrical and Electronics Engineers Inc., 2020-02-17

18
GANPU: A Versatile Many-Core Processor for Training GAN on Mobile Devices with Speculative Dual-Sparsity Exploitation

Kang, Sanghoon; Yoo, Hoi-Jun; Han, Donghyeon; LEE, JUHYOUNG; Im, DongSeok; Kim, Sangyeob; Kim, Soyeon; et al, Hot Chips: A Symposium on High Performance Chips, IEEE Technical Committee on Microprocessors and Microcomputers., 2020-08-16

19
UNPU: A 50.6TOPS/W Energy-Efficient Unified Deep Neural-Network Accelerator with 1-to-16b Fully Variable Bit Precision UNPU: A 50.6TOPS/W Energy-Efficient Unified Deep Neural-Network Accelerator with 1-to-16b Fully Variable Bit Precision

Lee, Jinmook; Kim, Changhyeon; Kang, Sanghoon; Shin, Dongjoo; Kim, Sangyeob; Yoo, Hoi-Jun, IEEE Internatioal Solid-State Circuits Conference, IEEE Internatioal Solid-State Circuits Conference, 2018-02

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