| Evaluations on Deep Neural Networks Training Using Posit Number System J Lu, C Fang, M Xu, J Lin, Z Wang IEEE Transactions on Computers 70 (2), 174-187, 2020 | 71 | 2020 |
| An Algorithm–Hardware Co-Optimized Framework for Accelerating N:M Sparse Transformers C Fang, A Zhou, Z Wang IEEE Transactions on Very Large Scale Integration (VLSI) Systems 30 (11 …, 2022 | 50 | 2022 |
| Training Deep Neural Networks Using Posit Number System J Lu, S Lu, Z Wang, C Fang, J Lin, Z Wang, L Du 2019 32nd IEEE International System-on-Chip Conference (SOCC), 62-67, 2019 | 25 | 2019 |
| BEBERT: Efficient and Robust Binary Ensemble BERT J Tian, C Fang, H Wang, Z Wang 2023 IEEE International Conference on Acoustics, Speech and Signal …, 2023 | 14 | 2023 |
| An Efficient Hardware Accelerator for Sparse Transformer Neural Networks C Fang, S Guo, W Wu, J Lin, Z Wang, MK Hsu, L Liu 2022 IEEE International Symposium on Circuits and Systems (ISCAS), 2670-2674, 2022 | 12 | 2022 |
| Accelerating 3D Convolutional Neural Networks Using 3D Fast Fourier Transform C Fang, L He, H Wang, J Wei, Z Wang 2021 IEEE International Symposium on Circuits and Systems (ISCAS), 1-5, 2021 | 11 | 2021 |
| A Precision-Scalable RISC-V DNN Processor with On-Device Learning Capability at the Extreme Edge L Huang, C Fang, Q Li, J Lin, Z Wang 2024 29th Asia and South Pacific Design Automation Conference (ASP-DAC), 927-932, 2024 | 9 | 2024 |
| Deep Learning in Environmental Toxicology: Current Progress and Open Challenges H Tan, J Jin, C Fang, Y Zhang, B Chang, X Zhang, H Yu, W Shi ACS ES&T Water 4 (3), 805-819, 2023 | 8 | 2023 |
| PDPU: An Open-Source Posit Dot-Product Unit for Deep Learning Applications Q Li, C Fang, Z Wang 2023 IEEE International Symposium on Circuits and Systems (ISCAS), 1-5, 2023 | 5 | 2023 |
| BETA: Binarized Energy-Efficient Transformer Accelerator at the Edge Y Ji, C Fang, Z Wang 2024 IEEE International Symposium on Circuits and Systems (ISCAS), 1-5, 2024 | 3 | 2024 |
| Efficient N:M Sparse DNN Training Using Algorithm, Architecture, and Dataflow Co-Design C Fang, W Sun, A Zhou, Z Wang IEEE Transactions on Computer-Aided Design of Integrated Circuits and …, 2023 | 3 | 2023 |
| CEST: Computation-Efficient N:M Sparse Training for Deep Neural Networks C Fang, W Sun, A Zhou, Z Wang 2023 Design, Automation & Test in Europe Conference & Exhibition (DATE), 1-2, 2023 | 3 | 2023 |
| A Configurable FPGA Accelerator of Bi-LSTM Inference with Structured Sparsity S Guo, C Fang, J Lin, Z Wang 2020 IEEE 33rd International System-on-Chip Conference (SOCC), 174-179, 2020 | 3 | 2020 |
| A Scalable RISC-V Vector Processor Enabling Efficient Multi-Precision DNN Inference C Wang, C Fang, X Wu, Z Wang, J Lin 2024 IEEE International Symposium on Circuits and Systems (ISCAS), 1-5, 2024 | 2 | 2024 |
| SPEED: A Scalable RISC-V Vector Processor Enabling Efficient Multiprecision DNN Inference C Wang, C Fang, X Wu, Z Wang, J Lin IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2024 | | 2024 |
| Efficient Arbitrary Precision Acceleration for Large Language Models on GPU Tensor Cores S Ma, C Fang, H Shao, Z Wang arXiv preprint arXiv:2409.17870, 2024 | | 2024 |
| Co-Designing Binarized Transformer and Hardware Accelerator for Efficient End-to-End Edge Deployment Y Ji*, C Fang*, S Ma, H Shao, Z Wang arXiv preprint arXiv:2407.12070, 2024 | | 2024 |
Zhongfeng WangNanjing University在 nju.edu.cn 的电子邮件经过验证
