Sun, Mengshu

SPViT: Enabling Faster Vision Transformers via Latency-Aware Soft Token Pruning

Kong, Z., Dong, P., Ma, X., Meng, X., Niu, W., Sun, M., Shen, X., Yuan, G., Ren, B., Tang, H., et al. (2022) · Lecture Notes in Computer Science

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 169

HeatViT: Hardware-Efficient Adaptive Token Pruning for Vision Transformers

Dong, P., Sun, M., Lu, A., Xie, Y., Liu, K., Kong, Z., Meng, X., Li, Z., Lin, X., Fang, Z., et al. (2023) · 2023 IEEE International Symposium on High-Performance Computer Architecture (HPCA)

Advanced Neural Network ApplicationsEmbedded Systems Design Techniques

Cited by 128

Auto-ViT-Acc: An FPGA-Aware Automatic Acceleration Framework for Vision Transformer with Mixed-Scheme Quantization

Li, Z., Sun, M., Lu, A., Ma, H., Yuan, G., Xie, Y., Tang, H., Li, Y., Leeser, M., Wang, Z., et al. (2022) · 2022 32nd International Conference on Field-Programmable Logic and Applications (FPL)

Advanced Neural Network ApplicationsEmbedded Systems Design Techniques

Cited by 85

VAQF: Fully Automatic Software-hardware Co-design Framework for Low-bit Vision Transformer

Sun, M., Ma, H., Kang, G., Jiang, Y., Chen, T., Ma, X., Wang, Z., & Wang, Y. (2022) · arXiv

Embedded Systems Design TechniquesAdvanced Neural Network Applications

Cited by 32