Sunwoo Lee
Orcid: 0000-0001-7760-0168Affiliations:
- Seoul National University, Korea
According to our database1,
Sunwoo Lee
authored at least 9 papers
between 2021 and 2024.
Collaborative distances:
Collaborative distances:
Timeline
2021
2022
2023
2024
0
1
2
3
4
1
1
2
2
2
1
Legend:
Book In proceedings Article PhD thesis Dataset OtherLinks
Online presence:
-
on orcid.org
On csauthors.net:
Bibliography
2024
A 5.6µW 10-Keyword End-to-End Keyword Spotting System Using Passive-Averaging SAR ADC and Sign-Exponent-Only Layer Fusion with 92.7% Accuracy.
Proceedings of the IEEE Symposium on VLSI Technology and Circuits 2024, 2024
ALAM: Averaged Low-Precision Activation for Memory-Efficient Training of Transformer Models.
Proceedings of the Twelfth International Conference on Learning Representations, 2024
2023
A0.81 mm<sup>2</sup> 740μW Real-Time Speech Enhancement Processor Using Multiplier-Less PE Arrays for Hearing Aids in 28nm CMOS.
Proceedings of the IEEE International Solid- State Circuits Conference, 2023
A 4.27TFLOPS/W FP4/FP8 Hybrid-Precision Neural Network Training Processor Using Shift-Add MAC and Reconfigurable PE Array.
Proceedings of the 49th IEEE European Solid State Circuits Conference, 2023
2022
A Neural Network Training Processor With 8-Bit Shared Exponent Bias Floating Point and Multiple-Way Fused Multiply-Add Trees.
IEEE J. Solid State Circuits, 2022
Toward Efficient Low-Precision Training: Data Format Optimization and Hysteresis Quantization.
Proceedings of the Tenth International Conference on Learning Representations, 2022
A low power neural network training processor with 8-bit floating point with a shared exponent bias and fused multiply add trees.
Proceedings of the 4th IEEE International Conference on Artificial Intelligence Circuits and Systems, 2022
2021
IEEE Trans. Very Large Scale Integr. Syst., 2021
A 40nm 4.81TFLOPS/W 8b Floating-Point Training Processor for Non-Sparse Neural Networks Using Shared Exponent Bias and 24-Way Fused Multiply-Add Tree.
Proceedings of the IEEE International Solid-State Circuits Conference, 2021