| Rosetta: A Realistic High-Level Synthesis Benchmark Suite for Software Programmable FPGAs Y Zhou, U Gupta, S Dai, R Zhao, N Srivastava, H Jin, J Featherston, ... Proceedings of the 2018 ACM/SIGDA International Symposium on Field …, 2018 | 199 | 2018 |
| Fast and Accurate Estimation of Quality of Results in High-Level Synthesis with Machine Learning S Dai, Y Zhou, H Zhang, E Ustun, EFY Young, Z Zhang 2018 IEEE 26th Annual International Symposium on Field-Programmable Custom …, 2018 | 175 | 2018 |
| Magnet: A modular accelerator generator for neural networks R Venkatesan, YS Shao, M Wang, J Clemons, S Dai, M Fojtik, B Keller, ... 2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 1-8, 2019 | 162 | 2019 |
| Softermax: Hardware/Software Co-Design of an Efficient Softmax for Transformers JR Stevens, R Venkatesan, S Dai, B Khailany, A Raghunathan 2021 58th ACM/IEEE Design Automation Conference (DAC), 469-474, 2021 | 159 | 2021 |
| The Celerity Open-Source 511-Core RISC-V Tiered Accelerator Fabric: Fast Architectures and Design Methodologies for Fast Chips S Davidson, S Xie, C Torng, K Al-Hawai, A Rovinski, T Ajayi, L Vega, ... IEEE Micro 38 (2), 30-41, 2018 | 158 | 2018 |
| VS-Quant: Per-vector Scaled Quantization for Accurate Low-Precision Neural Network Inference S Dai, R Venkatesan, H Ren, B Zimmer, WJ Dally, B Khailany arXiv preprint arXiv:2102.04503, 2021 | 110 | 2021 |
| Survey of machine learning for software-assisted hardware design verification: Past, present, and prospect N Wu, Y Li, H Yang, H Chen, S Dai, C Hao, C Yu, Y Xie ACM Transactions on Design Automation of Electronic Systems 29 (4), 1-42, 2024 | 105 | 2024 |
| Accelerating chip design with machine learning B Khailany Proceedings of the 2020 ACM/IEEE Workshop on Machine Learning for CAD, 33-33, 2020 | 100 | 2020 |
| Elasticflow: A complexity-effective approach for pipelining irregular loop nests M Tan, G Liu, R Zhao, S Dai, Z Zhang 2015 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 78-85, 2015 | 65 | 2015 |
| Optimal Clipping and Magnitude-aware Differentiation for Improved Quantization-aware Training C Sakr, S Dai, R Venkatesan, B Zimmer, W Dally, B Khailany International Conference on Machine Learning, 19123-19138, 2022 | 62 | 2022 |
| A 95.6-TOPS/W Deep Learning Inference Accelerator With Per-Vector Scaled 4-bit Quantization in 5 nm B Keller, R Venkatesan, S Dai, SG Tell, B Zimmer, C Sakr, WJ Dally, ... IEEE Journal of Solid-State Circuits 58 (4), 1129-1141, 2023 | 60 | 2023 |
| A 17–95.6 TOPS/W Deep Learning Inference Accelerator with Per-Vector Scaled 4-bit Quantization for Transformers in 5nm B Keller, R Venkatesan, S Dai, SG Tell, B Zimmer, WJ Dally, CT Gray, ... 2022 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and …, 2022 | 58 | 2022 |
| Gamora: Graph learning based symbolic reasoning for large-scale boolean networks N Wu, Y Li, C Hao, S Dai, C Yu, Y Xie 2023 60th ACM/IEEE Design Automation Conference (DAC), 1-6, 2023 | 50 | 2023 |
| Dynamic Hazard Resolution for Pipelining Irregular Loops in High-Level Synthesis S Dai, R Zhao, G Liu, S Srinath, U Gupta, C Batten, Z Zhang Proceedings of the 2017 ACM/SIGDA International Symposium on Field …, 2017 | 49 | 2017 |
| Celerity: An Open Source RISC-V Tiered Accelerator Fabric T Ajayi, K Al-Hawaj, A Amarnath, S Dai, S Davidson, P Gao, G Liu, A Lotfi, ... Symp. on High Performance Chips (Hot Chips), 2017 | 44 | 2017 |
| Evaluating celerity: A 16-nm 695 Giga-RISC-V instructions/s manycore processor with synthesizable PLL A Rovinski, C Zhao, K Al-Hawaj, P Gao, S Xie, C Torng, S Davidson, ... IEEE Solid-State Circuits Letters 2 (12), 289-292, 2019 | 43 | 2019 |
| A 1.4 GHz 695 Giga Risc-V inst/s 496-core manycore processor with mesh on-chip network and an all-digital synthesized PLL in 16nm CMOS A Rovinski, C Zhao, K Al-Hawaj, P Gao, S Xie, C Torng, S Davidson, ... 2019 Symposium on VLSI Circuits, C30-C31, 2019 | 43 | 2019 |
| Multithreaded pipeline synthesis for data-parallel kernels M Tan, B Liu, S Dai, Z Zhang 2014 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 718-725, 2014 | 40 | 2014 |
| Flushing-enabled loop pipelining for high-level synthesis S Dai, M Tan, K Hao, Z Zhang Proceedings of the 51st Annual Design Automation Conference, 1-6, 2014 | 40 | 2014 |
| High-level synthesis for low-power design Z Zhang, D Chen, S Dai, K Campbell IPSJ Transactions on System and LSI Design Methodology 8, 12-25, 2015 | 36 | 2015 |
Zhiru ZhangCornell UniversityVerified email at cornell.edu
