| A history-based auto-tuning framework for fast and high-performance DNN design on GPU J Mu, M Wang, L Li, J Yang, W Lin, W Zhang 2020 57th ACM/IEEE Design Automation Conference (DAC), 1-6, 2020 | 19 | 2020 |
| The impact of faulty memory bit cells on the decoding of spatially-coupled LDPC codes J Mu, A Vosoughi, J Andrade, A Balatsoukas-Stimming, G Karakonstantis, ... 2015 49th Asilomar Conference on Signals, Systems and Computers, 1627-1631, 2015 | 15 | 2015 |
| A collaborative framework for FPGA-based CNN design modeling and optimization J Mu, W Zhang, H Liang, S Sinha 2018 28th International Conference on Field Programmable Logic and …, 2018 | 10 | 2018 |
| A cost-effective CNN accelerator design with configurable PU on FPGA CFB Fong, J Mu, W Zhang 2019 IEEE Computer Society Annual Symposium on VLSI (ISVLSI), 31-36, 2019 | 9 | 2019 |
| Bayesian optimization with clustering and rollback for CNN auto pruning H Fan, J Mu, W Zhang European Conference on Computer Vision, 494-511, 2022 | 8 | 2022 |
| Optimizing OpenCL-based CNN design on FPGA with comprehensive design space exploration and collaborative performance modeling J Mu, W Zhang, H Liang, S Sinha ACM Transactions on Reconfigurable Technology and Systems (TRETS) 13 (3), 1-28, 2020 | 8 | 2020 |
| High-dimensional Bayesian optimization for CNN auto pruning with clustering and rollback J Mu, H Fan, W Zhang arXiv e-prints, arXiv: 2109.10591, 2021 | 3 | 2021 |
| Boosting the Convergence of Reinforcement Learning-based Auto-pruning Using Historical Data J Mu, M Wang, F Zhu, J Yang, W Lin, W Zhang IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2023 | 1 | 2023 |
| Optimization Frameworks for Compact DNN Acceleration Based on Collaborative Modeling and History Data J Mu PQDT-Global, 2021 | | 2021 |
