| 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 ACM SIGPLAN Notices 52 (4), 405-418, 2017 | 219 | 2017 |
| Admm-nn: An algorithm-hardware co-design framework of dnns using alternating direction methods of multipliers A Ren, T Zhang, S Ye, J Li, W Xu, X Qian, X Lin, Y Wang Proceedings of the Twenty-Fourth International Conference on Architectural …, 2019 | 160 | 2019 |
| VIBNN: Hardware acceleration of Bayesian neural networks R Cai, A Ren, N Liu, C Ding, L Wang, X Qian, M Pedram, Y Wang ACM SIGPLAN Notices 53 (2), 476-488, 2018 | 88 | 2018 |
| Towards acceleration of deep convolutional neural networks using stochastic computing J Li, A Ren, Z Li, C Ding, B Yuan, Q Qiu, Y Wang 2017 22nd Asia and South Pacific Design Automation Conference (ASP-DAC), 115-120, 2017 | 71 | 2017 |
| Deep reinforcement learning: Framework, applications, and embedded implementations H Li, T Wei, A Ren, Q Zhu, Y Wang 2017 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 847-854, 2017 | 69 | 2017 |
| 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, ... IEEE Transactions on Computer-Aided Design of Integrated Circuits and …, 2018 | 64 | 2018 |
| Dscnn: Hardware-oriented optimization for stochastic computing based deep convolutional neural networks Z Li, A Ren, J Li, Q Qiu, Y Wang, B Yuan 2016 IEEE 34th International Conference on Computer Design (ICCD), 678-681, 2016 | 60 | 2016 |
| 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 2017 International Joint Conference on Neural Networks (IJCNN), 1230-1236, 2017 | 51 | 2017 |
| Designing reconfigurable large-scale deep learning systems using stochastic computing A Ren, Z Li, Y Wang, Q Qiu, B Yuan 2016 IEEE International Conference on Rebooting Computing (ICRC), 1-7, 2016 | 44 | 2016 |
| Structural design optimization for deep convolutional neural networks using stochastic computing Z Li, A Ren, J Li, Q Qiu, B Yuan, J Draper, Y Wang Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017 …, 2017 | 43 | 2017 |
| 7.5 A 65nm 0.39-to-140.3 TOPS/W 1-to-12b unified neural network processor using block-circulant-enabled transpose-domain acceleration with 8.1× higher TOPS/mm 2 and 6T HBST … J Yue, R Liu, W Sun, Z Yuan, Z Wang, YN Tu, YJ Chen, A Ren, Y Wang, ... 2019 IEEE International Solid-State Circuits Conference-(ISSCC), 138-140, 2019 | 39 | 2019 |
| A stochastic-computing based deep learning framework using adiabatic quantum-flux-parametron superconducting technology R Cai, A Ren, O Chen, N Liu, C Ding, X Qian, J Han, W Luo, N Yoshikawa, ... Proceedings of the 46th International Symposium on Computer Architecture …, 2019 | 33 | 2019 |
| Normalization and dropout for stochastic computing-based deep convolutional neural networks J Li, Z Yuan, Z Li, A Ren, C Ding, J Draper, S Nazarian, Q Qiu, B Yuan, ... Integration 65, 395-403, 2019 | 28 | 2019 |
| Flexible clustered federated learning for client-level data distribution shift M Duan, D Liu, X Ji, Y Wu, L Liang, X Chen, Y Tan, A Ren IEEE Transactions on Parallel and Distributed Systems 33 (11), 2661-2674, 2021 | 27 | 2021 |
| A majority logic synthesis framework for adiabatic quantum-flux-parametron superconducting circuits R Cai, O Chen, A Ren, N Liu, C Ding, N Yoshikawa, Y Wang Proceedings of the 2019 on Great Lakes Symposium on VLSI, 189-194, 2019 | 24 | 2019 |
| Structured weight matrices-based hardware accelerators in deep neural networks: Fpgas and asics C Ding, A Ren, G Yuan, X Ma, J Li, N Liu, B Yuan, Y Wang Proceedings of the 2018 on Great Lakes Symposium on VLSI, 353-358, 2018 | 22 | 2018 |
| Improving dnn fault tolerance using weight pruning and differential crossbar mapping for reram-based edge ai G Yuan, Z Liao, X Ma, Y Cai, Z Kong, X Shen, J Fu, Z Li, C Zhang, H Peng, ... 2021 22nd International Symposium on Quality Electronic Design (ISQED), 135-141, 2021 | 21 | 2021 |
| Softmax regression design for stochastic computing based deep convolutional neural networks Z Yuan, J Li, Z Li, C Ding, A Ren, B Yuan, Q Qiu, J Draper, Y Wang Proceedings of the on Great Lakes Symposium on VLSI 2017, 467-470, 2017 | 21 | 2017 |
| CSAFL: A clustered semi-asynchronous federated learning framework Y Zhang, M Duan, D Liu, L Li, A Ren, X Chen, Y Tan, C Wang 2021 International Joint Conference on Neural Networks (IJCNN), 1-10, 2021 | 20 | 2021 |
| FedSAE: A novel self-adaptive federated learning framework in heterogeneous systems L Li, M Duan, D Liu, Y Zhang, A Ren, X Chen, Y Tan, C Wang 2021 International Joint Conference on Neural Networks (IJCNN), 1-10, 2021 | 20 | 2021 |
Yanzhi WangNortheastern UniversityVerified email at northeastern.edu
