| A hierarchical framework of cloud resource allocation and power management using deep reinforcement learning N Liu, Z Li, J Xu, Z Xu, S Lin, Q Qiu, J Tang, Y Wang 2017 IEEE 37th international conference on distributed computing systems …, 2017 | 385 | 2017 |
| Patdnn: Achieving real-time dnn execution on mobile devices with pattern-based weight pruning W Niu, X Ma, S Lin, S Wang, X Qian, X Lin, Y Wang, B Ren Proceedings of the Twenty-Fifth International Conference on Architectural …, 2020 | 336 | 2020 |
| Non-Structured DNN Weight Pruning–Is It Beneficial in Any Platform? X Ma, S Lin, S Ye, Z He, L Zhang, G Yuan, S Huat Tan, Z Li, D Fan, X Qian, ... TNNLS, arXiv: 1907.02124, 2022 | 151 | 2022 |
| CAP-RAM: A charge-domain in-memory computing 6T-SRAM for accurate and precision-programmable CNN inference Z Chen, Z Yu, Q Jin, Y He, J Wang, S Lin, D Li, Y Wang, K Yang IEEE Journal of Solid-State Circuits 56 (6), 1924-1935, 2021 | 129 | 2021 |
| Forms: Fine-grained polarized reram-based in-situ computation for mixed-signal dnn accelerator G Yuan, P Behnam, Z Li, A Shafiee, S Lin, X Ma, H Liu, X Qian, ... 2021 ACM/IEEE 48th Annual International Symposium on Computer Architecture …, 2021 | 105 | 2021 |
| FFT-based deep learning deployment in embedded systems S Lin, N Liu, M Nazemi, H Li, C Ding, Y Wang, M Pedram 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE …, 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, ... 2019 IEEE/ACM International Symposium on Low Power Electronics and Design …, 2019 | 68 | 2019 |
| Rtmobile: Beyond real-time mobile acceleration of rnns for speech recognition P Dong, S Wang, W Niu, C Zhang, S Lin, Z Li, Y Gong, B Ren, X Lin, ... 2020 57th ACM/IEEE Design Automation Conference (DAC), 1-6, 2020 | 67 | 2020 |
| Tiny but accurate: A pruned, quantized and optimized memristor crossbar framework for ultra efficient dnn implementation X Ma, G Yuan, S Lin, C Ding, F Yu, T Liu, W Wen, X Chen, Y Wang 2020 25th Asia and South Pacific design automation conference (ASP-DAC), 301-306, 2020 | 66 | 2020 |
| Progressive dnn compression: A key to achieve ultra-high weight pruning and quantization rates using admm S Ye, X Feng, T Zhang, X Ma, S Lin, Z Li, K Xu, W Wen, S Liu, J Tang, ... arXiv preprint arXiv:1903.09769, 2019 | 65 | 2019 |
| Resnet can be pruned 60×: Introducing network purification and unused path removal (p-rm) after weight pruning X Ma, G Yuan, S Lin, Z Li, H Sun, Y Wang 2019 IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH), 1-2, 2019 | 53 | 2019 |
| An image enhancing pattern-based sparsity for real-time inference on mobile devices X Ma, W Niu, T Zhang, S Liu, S Lin, H Li, W Wen, X Chen, J Tang, K Ma, ... European Conference on Computer Vision, 629-645, 2020 | 40 | 2020 |
| NS-FDN: Near-sensor processing architecture of feature-configurable distributed network for beyond-real-time always-on keyword spotting Q Li, C Liu, P Dong, Y Zhang, T Li, S Lin, M Yang, F Qiao, Y Wang, L Luo, ... IEEE Transactions on Circuits and Systems I: Regular Papers 68 (5), 1892-1905, 2021 | 35 | 2021 |
| ADMM-based weight pruning for real-time deep learning acceleration on mobile devices H Li, N Liu, X Ma, S Lin, S Ye, T Zhang, X Lin, W Xu, Y Wang Proceedings of the 2019 Great Lakes Symposium on VLSI, 501-506, 2019 | 35 | 2019 |
| When sorting network meets parallel bitstreams: A fault-tolerant parallel ternary neural network accelerator based on stochastic computing Y Zhang, S Lin, R Wang, Y Wang, Y Wang, W Qian, R Huang 2020 Design, Automation & Test in Europe Conference & Exhibition (DATE …, 2020 | 32 | 2020 |
| Fft-based deep learning deployment in embedded systems. In 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE) S Lin, N Liu, M Nazemi, H Li, C Ding, Y Wang, M Pedram IEEE, 1045ś1050, 2018 | 19 | 2018 |
| Dynamic Reconfiguration of Thermoelectric Generators for Vehicle Radiators Energy Harvesting Under Location-Dependent Temperature Variations J Kim, D Baek, C Ding, S Lin, D Shin, X Lin, Y Wang, YH Cho, SH Park, ... | 19 | 2018 |
| Darb: A density-adaptive regular-block pruning for deep neural networks R Ao, Z Tao, W Yuhao, L Sheng, D Peiyan, C Yen-kuang, X Yuan, ... Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 5495-5502, 2020 | 17 | 2020 |
| Toward Extremely Low Bit and Lossless Accuracy in DNNs with Progressive ADMM S Lin, X Ma, S Ye, G Yuan, K Ma, Y Wang ICML workshop arXiv preprint arXiv:1905.00789, 2019 | 16 | 2019 |
| A secure and efficient federated learning framework for NLP C Wang, J Deng, X Meng, Y Wang, J Li, S Lin, S Han, F Miao, ... Proceedings of the 2021 Conference on Empirical Methods in Natural Language …, 2021 | 15 | 2021 |
Yanzhi WangNortheastern UniversityVerified email at northeastern.edu
