| CirCNN: accelerating and compressing deep neural networks using block-circulant weight matrices C Ding, S Liao, Y Wang, Z Li, N Liu, Y Zhuo, C Wang, X Qian, Y Bai, ... (MICRO) Proceedings of the 50th Annual IEEE/ACM International Symposium on …, 2017 | 374 | 2017 |
| Sc-dcnn: Highly-scalable deep convolutional neural network using stochastic computing A Ren, Z Li, C Ding, Q Qiu, Y Wang, J Li, X Qian, B Yuan (ASPLOS) 2017 Intl Conf on Architectural Support for Programming Languages …, 2017 | 300 | 2017 |
| C-lstm: Enabling efficient lstm using structured compression techniques on fpgas S Wang, Z Li, C Ding, B Yuan, Q Qiu, Y Wang, Y Liang (FPGA) Proceedings of the 2018 ACM/SIGDA International Symposium on Field …, 2018 | 274 | 2018 |
| FTRANS: Energy-Efficient Acceleration of Transformers using FPGA B Li, S Pandey, H Fang, Y Lyv, J Li, J Chen, M Xie, L Wan, H Liu, C Ding (ISLPED) 2020 Proceedings of the ACM/IEEE International Symposium on Low …, 2020 | 259 | 2020 |
| VIBNN: Hardware acceleration of Bayesian neural networks R Cai, A Ren, N Liu, C Ding, L Wang, X Qian, M Pedram, Y Wang (ASPLOS) Proceedings of the 23rd International Conference on Architectural …, 2018 | 146 | 2018 |
| REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs C Ding, S Wang, N Liu, K Xu, Y Wang, Y Liang (FPGA) Proceedings of the 2019 ACM/SIGDA International Symposium on Field …, 2019 | 141 | 2019 |
| Accelerating transformer-based deep learning models on fpgas using column balanced block pruning H Peng, S Huang, T Geng, A Li, W Jiang, H Liu, S Wang, C Ding 2021 22nd International Symposium on Quality Electronic Design (ISQED), 142-148, 2021 | 132 | 2021 |
| Tag: Gradient attack on transformer-based language models J Deng, Y Wang, J Li, C Shang, H Liu, S Rajasekaran, C Ding EMNLP, 2021 | 115 | 2021 |
| FORMS: Fine-grained Polarized ReRAM-based In-situ Computation for Mixed-signal DNN Accelerator G Yuan, P Behnam, Z Li, A Shafiee, X Ma, H Liu, X Qian, M Bojnordi, ... (ISCA'21) The 48th International Symposium on Computer Architecture, 2021, 2021 | 105 | 2021 |
| HEIF: Highly efficient stochastic computing-based inference framework for deep neural networks Z Li, J Li, A Ren, R Cai, C Ding, X Qian, J Draper, B Yuan, J Tang, Q Qiu, ... (TCAD) IEEE Transactions on Computer-Aided Design of Integrated Circuits and …, 2018 | 105 | 2018 |
| E-RNN: Design optimization for efficient recurrent neural networks in FPGAs Z Li, C Ding, S Wang, W Wen, Y Zhuo, C Liu, Q Qiu, W Xu, X Lin, X Qian, ... (HPCA) 2019 IEEE International Symposium on High Performance Computer …, 2019 | 103 | 2019 |
| Towards acceleration of deep convolutional neural networks using stochastic computing J Li, A Ren, Z Li, C Ding, B Yuan, Q Qiu, Y Wang (ASP-DAC) 2017 22nd Asia and South Pacific Design Automation Conference, 115-120, 2017 | 99 | 2017 |
| Accelerating dataset distillation via model augmentation L Zhang, J Zhang, B Lei, S Mukherjee, X Pan, B Zhao, C Ding, Y Li, D Xu (CVPR) Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 97 | 2023 |
| Uncertainty quantification of collaborative detection for self-driving S Su, Y Li, S He, S Han, C Feng, C Ding, F Miao arXiv preprint arXiv:2209.08162, 2022 | 86 | 2022 |
| Against Membership Inference Attack: Pruning is All You Need Y Wang, C Wang, Z Wang, S Zhou, H Liu, J Bi, C Ding, S Rajasekaran (IJCAI) In Proceedings of the 30th International Joint Conference on …, 2021 | 77 | 2021 |
| Quclassi: A hybrid deep neural network architecture based on quantum state fidelity SA Stein, B Baheri, D Chen, Y Mao, Q Guan, A Li, S Xu, C Ding Proceedings of Machine Learning and Systems 4, 251-264, 2022 | 75 | 2022 |
| A length adaptive algorithm-hardware co-design of transformer on fpga through sparse attention and dynamic pipelining H Peng, S Huang, S Chen, B Li, T Geng, A Li, W Jiang, W Wen, J Bi, H Liu, ... (DAC) Proceedings of the 59th ACM/IEEE Design Automation Conference, 1135-1140, 2022 | 72 | 2022 |
| Fft-based deep learning deployment in embedded systems S Lin, N Liu, M Nazemi, H Li, C Ding, Y Wang, M Pedram (DATE) 2018 Design, Automation & Test in Europe Conference & Exhibition …, 2018 | 71 | 2018 |
| An Ultra-Efficient Memristor-Based DNN Framework with Structured Weight Pruning and Quantization Using ADMM G Yuan, X Ma, C Ding, S Lin, T Zhang, ZS Jalali, Y Zhao, L Jiang, ... (ISLPED) 2019 IEEE/ACM International Symposium on Low Power Electronics and …, 2019 | 68 | 2019 |
| Hardware-driven nonlinear activation for stochastic computing based deep convolutional neural networks J Li, Z Yuan, Z Li, C Ding, A Ren, Q Qiu, J Draper, Y Wang (IJCNN) 2017 International Joint Conference on Neural Networks, 1230-1236, 2017 | 67 | 2017 |
Yanzhi WangNortheastern UniversityVerified email at northeastern.edu
