( en | de | ru )

 

Disambiguation of "Shin, Dongjoo"

Author of the publication

copydeleteadd this publication to your clipboard
A Full HD 60 fps CNN Super Resolution Processor with Selective Caching based Layer Fusion for Mobile Devices.

, , , , , and . VLSI Circuits, page 302-. IEEE, (2019)

Please choose a person to relate this publication to

To differ between persons with the same name, the academic degree and the title of an important publication will be displayed. You can also use the button next to the name to display some publications already assigned to the person.

Dong-Shin Shin

Jeonghun Shin

Kirche als Weltforum: zum Dialogverständnis in kirchlichen Dokumenten seit dem Zweiten Vatikanischen Konzil. Uni München, (2009)

Hwa-Ja Shin

Jung-Hyu Shin

Hyeseon Shin

 

Other publications of authors with the same name

An 1.92mW Feature Reuse Engine based on inter-frame similarity for low-power object recognition in video frames., , and . ISCAS, page 758-761. IEEE, (2014)Flexible Sensor Platform: Nano-grain of 2D Heterostructure by Cold-Plasma., , , and . MeMeA, page 1-6. IEEE, (2022)18.5 A 2.14mW EEG neuro-feedback processor with transcranial electrical stimulation for mental-health management., , , , , , and . ISSCC, page 318-319. IEEE, (2014)A 1.1 mW 32-thread artificial intelligence processor with 3-level transposition table and on-chip PVT compensation for autonomous mobile robots., , , and . COOL Chips, page 1-2. IEEE Computer Society, (2016)UNPU: An Energy-Efficient Deep Neural Network Accelerator With Fully Variable Weight Bit Precision., , , , , and . IEEE J. Solid State Circuits, 54 (1): 173-185 (2019)Wearable Sensor based on Fiber Bragg Grating with Flexible Polymer for Squat Exercise., and . MetroInd4.0&IoT, page 478-481. IEEE, (2021)14.3 A 0.55V 1.1mW artificial-intelligence processor with PVT compensation for micro robots., , , , and . ISSCC, page 258-259. IEEE, (2016)14.2 DNPU: An 8.1TOPS/W reconfigurable CNN-RNN processor for general-purpose deep neural networks., , , and . ISSCC, page 240-241. IEEE, (2017)A 21mW low-power recurrent neural network accelerator with quantization tables for embedded deep learning applications., , and . A-SSCC, page 237-240. IEEE, (2017)A 1.41mW on-chip/off-chip hybrid transposition table for low-power robust deep tree search in artificial intelligence SoCs., , and . SoCC, page 138-142. IEEE, (2017)