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Wooseok Yi
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2020 – today
- 2022
- [j3]Sungju Ryu, Hyungjun Kim, Wooseok Yi, Eunhwan Kim, Yulhwa Kim, Taesu Kim, Jae-Joon Kim:
BitBlade: Energy-Efficient Variable Bit-Precision Hardware Accelerator for Quantized Neural Networks. IEEE J. Solid State Circuits 57(6): 1924-1935 (2022) - 2020
- [j2]Junki Park, Wooseok Yi, Daehyun Ahn, Jaeha Kung, Jae-Joon Kim:
Balancing Computation Loads and Optimizing Input Vector Loading in LSTM Accelerators. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 39(9): 1889-1901 (2020) - [j1]Wooseok Yi, Junki Park, Jae-Joon Kim:
GeCo: Classification Restricted Boltzmann Machine Hardware for On-Chip Semisupervised Learning and Bayesian Inference. IEEE Trans. Neural Networks Learn. Syst. 31(1): 53-65 (2020) - [c5]Sungju Ryu, Hyungjun Kim, Wooseok Yi, Jongeun Koo, Eunhwan Kim, Yulhwa Kim, Taesu Kim, Jae-Joon Kim:
A 44.1TOPS/W Precision-Scalable Accelerator for Quantized Neural Networks in 28nm CMOS. CICC 2020: 1-4
2010 – 2019
- 2019
- [c4]Sungju Ryu, Hyungjun Kim, Wooseok Yi, Jae-Joon Kim:
BitBlade: Area and Energy-Efficient Precision-Scalable Neural Network Accelerator with Bitwise Summation. DAC 2019: 84 - [c3]Wooseok Yi, Yulhwa Kim, Jae-Joon Kim:
Effect of Device Variation on Mapping Binary Neural Network to Memristor Crossbar Array. DATE 2019: 320-323 - 2018
- [c2]Junki Park, Jaeha Kung, Wooseok Yi, Jae-Joon Kim:
Maximizing system performance by balancing computation loads in LSTM accelerators. DATE 2018: 7-12 - 2017
- [c1]Wooseok Yi, Junki Park, Jae-Joon Kim:
GeCo: classification restricted Boltzmann machine hardware for on-chip learning. RSP 2017: 30-35
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