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Ming-Guang Lin
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2020 – today
- 2024
- [c7]Jiing-Ping Wang, Ming-Guang Lin, An-Yeu Andy Wu:
LATTE: Low-Precision Approximate Attention with Head-wise Trainable Threshold for Efficient Transformer. AICAS 2024: 208-212 - [c6]Ming-Guang Lin, Jiing-Ping Wang, Cheng-Yang Chang, An-Yeu Andy Wu:
Approximate Adder Tree Design with Sparsity-Aware Encoding and In-Memory Swapping for SRAM-based Digital Compute-In-Memory Macros. AICAS 2024: 362-366 - [c5]Yu-Chuan Chuang, Ming-Guang Lin, Chi-Tse Huang, Chieh-Fang Teng, Cheng-Yang Chang, Yi-Ta Chen, An-Yeu Andy Wu:
A 40nm 24.6TOPS/W Scalable EfficientDet Processor for Object Detection. ISCAS 2024: 1-5 - [c4]Yuan-June Luo, Yu-Shan Tai, Ming-Guang Lin, An-Yeu Andy Wu:
Similarity-Aware Fast Low-Rank Decomposition Framework for Vision Transformers. ISCAS 2024: 1-5 - [c3]Yun-Chia Yu, Mao-Chi Weng, Ming-Guang Lin, An-Yeu Andy Wu:
Retraining-free Constraint-aware Token Pruning for Vision Transformer on Edge Devices. ISCAS 2024: 1-5 - [i1]Jiing-Ping Wang, Ming-Guang Lin, An-Yeu Wu:
LATTE: Low-Precision Approximate Attention with Head-wise Trainable Threshold for Efficient Transformer. CoRR abs/2404.07519 (2024) - 2023
- [c2]Yu-Shan Tai, Ming-Guang Lin, An-Yeu Andy Wu:
TSPTQ-ViT: Two-Scaled Post-Training Quantization for Vision Transformer. ICASSP 2023: 1-5 - 2022
- [j1]Ming-Guang Lin
, Chi-Tse Huang
D-NAT: Data-Driven Non-Ideality Aware Training Framework for Fabricated Computing-In-Memory Macros. IEEE J. Emerg. Sel. Topics Circuits Syst. 12(2): 381-392 (2022) - [c1]Chi-Tse Huang, Yu-Chuan Chuang, Ming-Guang Lin, An-Yeu Andy Wu:
Automated Quantization Range Mapping for DAC/ADC Non-linearity in Computing-In-Memory. ISCAS 2022: 2998-3002
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