Scholar
Guohao Dai
Google Scholar ID: gz3Tkl0AAAAJ
Associate Professor of Shanghai Jiao Tong University
Citations & Impact
Citations
2,731
H-index
28
i10-index
53
Publications
20
Co-authors
30
list available
Publications
Resume (English only)
Academic Achievements
- Paper 'A Cross-model Fusion-aware Framework for Optimizing (gather-matmul-scatter)s Workload' accepted by Design Automation Conference (DAC) 2025.
- Paper 'Enabling Efficient Sparse Multiplications on GPUs with Heuristic Adaptability' published in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD) 2025.
- Paper 'Endor: Exploit Nearly-Decode-Only Opportunities of LLM Reasoning on Near-Memory Architecture' to appear in Design, Automation and Test in Europe (DATE) 2026.
- Paper 'FlightVGM: Efficient Video Generation Model Inference with Online Sparsification and Hybrid Precision on FPGAs' presented at 33rd ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (FPGA) 2025.
- Paper 'SpecEE: Accelerating Large Language Model Inference with Speculative Early Exiting' presented at The International Symposium on Computer Architecture (ISCA) 2025.
- Paper 'FlightLLM: Efficient Large Language Model Inference with a Complete Mapping Flow on FPGAs' presented at International Symposium on Field-Programmable Gate Arrays (FPGA) 2024.
- Video generation model sparsification accelerator ViDA won the ASP-DAC'25 Best Paper Award.
- Video generation large model inference IP FlightVGM won the FPGA'25 Best Paper Award.
- Multiple papers accepted by DATE 2026 and AAAI 2026.
Research Experience
- Serves as an Associate Professor at the School of Electronic Information and Electrical Engineering, Qingyuan Research Institute, Shanghai Jiao Tong University, leading the Design Automation Innovation & Domain-specific Artificial Intelligence (DAI) Group. The group currently has about 10 PhD/master students and collaborates closely with Prof. Yu Wang's (Chair of Department of Electronic Engineering, IEEE Fellow) team at Tsinghua University.
Education
- No specific educational background information provided.
Background
- Research interests include exploring circuit/architecture/system design methodologies for emerging AI applications such as large-scale sparse graph applications, large language models, autonomous driving, recommendation systems, etc.
Miscellany
- Interested individuals are encouraged to email Prof. Dai to express their interest in the DAI group's research.
