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Showing results 1 to 7 of 7

1
A 47.4µJ/epoch Trainable Deep Convolutional Neural Network Accelerator for In-Situ Personalization on Smart Devices

Choi, Seungkyu; Sim, Jaehyeong; Kang, Myeonggu; Choi, Yeongjae; Kim, Hyeonuk; Kim, Lee-Sup, 2019 IEEE Asian Solid-State Circuits Conference, IEEE/SSCS, 2019-11-05

2
A Kernel Decomposition Architecture for Binary-weight Convolutional Neural Netwarks

Kim, Hyeonuk; Sim, Jaehyeong; Choi, Yeongjae; Kim, Lee-Sup, 54th ACM/EDAC/IEEE Design Automation Conference (DAC), ACM Special Interest Group on Design Automation (SIGDA), 2017-06

3
A pragmatic approach to on-device incremental learning system with selective weight updates

Shin, Jaekang; Choi, Seungkyu; Choi, Yeongjae; Kim, Lee-Sup, 57th ACM/IEEE Design Automation Conference, DAC 2020, Institute of Electrical and Electronics Engineers Inc., 2020-07

4
An optimized design technique of low-bit neural network training for personalization on IoT devices

Choi, Seungkyu; Shin, Jaekang; Choi, Yeongjae; Kim, Lee-Sup, 56th ACM/EDAC/IEEE Design Automation Conference (DAC), Institute of Electrical and Electronics Engineers Inc., 2019-06-06

5
eSRCNN: A Framework for Optimizing Super-Resolution Tasks on Diverse Embedded CNN Accelerators

Jung, Youngbeom; Choi, Yeongjae; Sim, Jaehyeong; Kim, Lee-Sup, 38th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2019, IEEE/ACM, 2019-11-04

6
Hardware-Centric Vision Processing for Mobile IoT Environment Exploiting Approximate Graph cut in Resistor Grid

Choi, Yeongjae; Park, Jun-Seok; Kim, Lee-Sup, 17th IEEE Winter Conference on Applications of Computer Vision (WACV), IEEE Computer Society, IEEE Biometrics Council, 2017-03

7
NAND-Net: Minimizing Computational Complexity of In-Memory Processing for Binary Neural Networks

Kim, Hyeonuk; Sim, Jaehyeong; Choi, Yeongjae; Kim, Lee-Sup, 2019 IEEE International Symposium on High-Performance Computer Architecture, pp.661 - 673, IEEE/ACM, 2019-02

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