Peng, Hongwu

Accelerating Transformer-based Deep Learning Models on FPGAs using Column Balanced Block Pruning

Peng, H., Huang, S., Geng, T., Li, A., Jiang, W., Liu, H., Wang, S., & Ding, C. (2021) · 2021 22nd International Symposium on Quality Electronic Design (ISQED)

Advanced Neural Network ApplicationsEmbedded Systems Design Techniques

Cited by 105

A length adaptive algorithm-hardware co-design of transformer on FPGA through sparse attention and dynamic pipelining

Peng, H., Huang, S., Chen, S., Li, B., Geng, T., Li, A., Jiang, W., Wen, W., Bi, J., Liu, H., et al. (2022) · Proceedings of the 59th ACM/IEEE Design Automation Conference

Embedded Systems Design TechniquesAdvanced Neural Network Applications

Cited by 48

Accommodating Transformer onto FPGA: Coupling the Balanced Model Compression and FPGA-Implementation Optimization

Qi, P., Song, Y., Peng, H., Huang, S., Zhuge, Q., & Sha, E. H.-M. (2021) · Proceedings of the 2021 Great Lakes Symposium on VLSI

Embedded Systems Design TechniquesAdvanced Neural Network Applications

Cited by 37

Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads

Cai, T., Li, Y., Geng, Z., Peng, H., Lee, J. D., Chen, D., & Dao, T. (2024) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 27

HMC-T RAN: A Tensor-core Inspired Hierarchical Model Compression for Transformer-based DNNs on GPU

Huang, S., Chen, S., Peng, H., Manu, D., Kong, Z., Yuan, G., Yang, L., Wang, S., Liu, H., & Ding, C. (2021) · Proceedings of the 2021 Great Lakes Symposium on VLSI

Advanced Neural Network ApplicationsParallel Computing and Optimization Techniques

Cited by 4

Two Heads are Better Than One: Test-time Scaling of Multi-agent Collaborative Reasoning

Jin, C., Peng, H., Zhang, Q., Zhou, Y., Tang, Y., Che, T., & Metaxas, D. N. (2025) · arXiv

Topic ModelingMulti-Agent Systems and Negotiation

Cited by 0