Jinmook Lee
Orcid: 0000-0002-7693-8148
According to our database1,
Jinmook Lee
authored at least 23 papers
between 2015 and 2020.
Collaborative distances:
Collaborative distances:
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Bibliography
2020
IEEE Trans. Circuits Syst., 2020
The Hardware and Algorithm Co-Design for Energy-Efficient DNN Processor on Edge/Mobile Devices.
IEEE Trans. Circuits Syst., 2020
A 146.52 TOPS/W Deep-Neural-Network Learning Processor with Stochastic Coarse-Fine Pruning and Adaptive Input/Output/Weight Skipping.
Proceedings of the IEEE Symposium on VLSI Circuits, 2020
2019
A Low-Power Deep Neural Network Online Learning Processor for Real-Time Object Tracking Application.
IEEE Trans. Circuits Syst. I Regul. Pap., 2019
UNPU: An Energy-Efficient Deep Neural Network Accelerator With Fully Variable Weight Bit Precision.
IEEE J. Solid State Circuits, 2019
A Full HD 60 fps CNN Super Resolution Processor with Selective Caching based Layer Fusion for Mobile Devices.
Proceedings of the 2019 Symposium on VLSI Circuits, Kyoto, Japan, June 9-14, 2019, 2019
A 1.32 TOPS/W Energy Efficient Deep Neural Network Learning Processor with Direct Feedback Alignment based Heterogeneous Core Architecture.
Proceedings of the 2019 Symposium on VLSI Circuits, Kyoto, Japan, June 9-14, 2019, 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
2018
DNPU: An Energy-Efficient Deep-Learning Processor with Heterogeneous Multi-Core Architecture.
IEEE Micro, 2018
Low-Power Scalable 3-D Face Frontalization Processor for CNN-Based Face Recognition in Mobile Devices.
IEEE J. Emerg. Sel. Topics Circuits Syst., 2018
B-Face: 0.2 MW CNN-Based Face Recognition Processor with Face Alignment for Mobile User Identification.
Proceedings of the 2018 IEEE Symposium on VLSI Circuits, 2018
UNPU: A 50.6TOPS/W unified deep neural network accelerator with 1b-to-16b fully-variable weight bit-precision.
Proceedings of the 2018 IEEE International Solid-State Circuits Conference, 2018
A 141.4 mW Low-Power Online Deep Neural Network Training Processor for Real-time Object Tracking in Mobile Devices.
Proceedings of the IEEE International Symposium on Circuits and Systems, 2018
2017
An Energy-Efficient Speech-Extraction Processor for Robust User Speech Recognition in Mobile Head-Mounted Display Systems.
IEEE Trans. Circuits Syst. II Express Briefs, 2017
14.2 DNPU: An 8.1TOPS/W reconfigurable CNN-RNN processor for general-purpose deep neural networks.
Proceedings of the 2017 IEEE International Solid-State Circuits Conference, 2017
A 0.53mW ultra-low-power 3D face frontalization processor for face recognition with human-level accuracy in wearable devices.
Proceedings of the IEEE International Symposium on Circuits and Systems, 2017
An energy-efficient deep learning processor with heterogeneous multi-core architecture for convolutional neural networks and recurrent neural networks.
Proceedings of the 2017 IEEE Symposium in Low-Power and High-Speed Chips, 2017
A 21mW low-power recurrent neural network accelerator with quantization tables for embedded deep learning applications.
Proceedings of the IEEE Asian Solid-State Circuits Conference, 2017
2016
14.1 A 126.1mW real-time natural UI/UX processor with embedded deep-learning core for low-power smart glasses.
Proceedings of the 2016 IEEE International Solid-State Circuits Conference, 2016
An 8.3mW 1.6Msamples/s multi-modal event-driven speech enhancement processor for robust speech recognition in smart glasses.
Proceedings of the ESSCIRC Conference 2016: 42<sup>nd</sup> European Solid-State Circuits Conference, 2016
2015
An Energy-Efficient and Scalable Deep Learning/Inference Processor With Tetra-Parallel MIMD Architecture for Big Data Applications.
IEEE Trans. Biomed. Circuits Syst., 2015
4.6 A1.93TOPS/W scalable deep learning/inference processor with tetra-parallel MIMD architecture for big-data applications.
Proceedings of the 2015 IEEE International Solid-State Circuits Conference, 2015
A 3.13nJ/sample energy-efficient speech extraction processor for robust speech recognition in mobile head-mounted display systems.
Proceedings of the 2015 IEEE International Symposium on Circuits and Systems, 2015