• Ashutosh Dhar, Edward Richter *, Mang Yu *, Wei Zuo, Xiaohao Wang, Nam Sung Kim, and Deming Chen, “DML: Dynamic Partial Reconfiguration with Scalable Task Scheduling for Multi-Applications on FPGAs”, IEEE Transactions on Computers. To appear.
    (* Equal Contribution)
  • Hanchen Ye, Cong Hao, Jianyi Cheng, Hyunmin Jeong, Jack Huang, Stephen Neuendorffer, and Deming Chen, “ScaleHLS: A New Scalable High-Level Synthesis Framework on Multi-Level Intermediate Representation,” Proceedings of IEEE International Symposium on High-Performance Computer Architecture, April 2022.
  • Xinheng Liu, Yao Chen, Prakhar Ganesh, Junhao Pan, Jinjun Xiong, and Deming Chen, “HiKonv: High Throughput Quantized Convolution with Novel Bit-wise Management and Computation”, Proceedings of IEEE/ACM Asia and South Pacific Design Automation Conference, January 2022.
  • Sitao Huang, Kun Wu, Hyunmin Jeong, Chengyue Wang, Deming Chen, and Wen-Mei Hwu, “PyLog: An Algorithm-Centric Python-Based FPGA Programming and Synthesis Flow,” IEEE Transactions on Computers, Vol. 70, No. 12, December 2021.
  • Sitao Huang, Kun Wu, Sai Rahul Chalamalasetti, Izzat El Hajj, Cong Xu, Paolo Faraboschi, and Deming Chen, “A Python-based High-Level Programming Flow for CPU-FPGA Heterogeneous Systems,” Proceedings of the Workshop for Programming Environments for Heterogeneous Computing (co-located with SC21), November 2021.
  • Mang Yu, Sitao Huang, and Deming Chen, “Chimera: A Hybrid Machine Learning-Driven Multi-Objective Design Space Exploration Tool for FPGA High-Level Synthesis”, Proceedings of International Conference on Intelligent Data Engineering and Automated Learning, November 2021. (Best Paper Award)
  • Seung Won Min, Kun Wu, Sitao Huang, Mert Hidayetoğlu, Jinjun Xiong, Eiman Ebrahimi, Deming Chen, and Wen-mei Hwu, “Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecture”, Proceedings of VLDB Endowment (PVLDB), Volume 14, Issue 11, July 2021.
  • Hyunmin Jeong and Deming Chen, “TwinDNN: A Tale of Two Deep Neural Networks”, Proceedings of IEEE International Conference on Application-specific Systems, Architectures and Processors, July 2021.
  • Ashutosh Dhar, Paul Reckamp, Jinjun Xiong, Wen-mei Hwu, and Deming Chen, “Graviton: A Reconfigurable Memory-Compute Fabric for Data Intensive Applications”, Proceedings of International Symposium on Applied Reconfigurable Computing, June 2021.
  • Xinyu Chen, Hongshi Tan, Yao Chen, Bingsheng He, Weng-Fai Wong, and Deming Chen, “ThunderGP: HLS-based Graph Processing Framework on FPGAs”, Proceedings of ACM/SIGDA International Symposium on Field Programmable Gate Arrays, February 2021. DOI:
  • Yichi Zhang, Junhao Pan, Xinheng Liu, Hongzheng Chen, Deming Chen and Zhiru Zhang, “FracBNN: Accurate and FPGA-Efficient Binary Neural Networks with Fractional Activations,” Proceedings of ACM/SIGDA International Symposium on Field Programmable Gate Arrays, February 2021. (Best Paper Candidate). DOI:
  • Junhao Pan and Deming Chen, “Accelerate Non-unit Stride Convolutions with Winograd Algorithms”, Proceedings of IEEE/ACM Asia and South Pacific Design Automation Conference, January 2021. DOI:
  • Seung Won Min, Vikram Sharma Mailthody, Zaid Qureshi, Jinjun Xiong, Eiman Ebrahimi, and Wen-mei Hwu. 2020. EMOGI: efficient memory-access for out-of-memory graph-traversal in GPUs. Proc. VLDB Endow. 14, 2 (October 2020), 114–127. DOI:
  • Ashutosh Dhar, Xiaohao Wang, Hubertus Franke, Jinjun Xiong, Jian Huang, Wen–mei Hwu, Nam Sung Kim, Deming Chen, “FReaC Cache: Folded-logic Reconfigurable Computing in the Last Level Cache,” 2020 53rd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO), Athens, Greece, 2020, pp. 102-117, doi: 10.1109/MICRO50266.2020.00021.
  • Subho S. Banerjee, Saurabh Jha, Zbigniew T. Kalbarczyk, and Ravishankar K. Iyer. “Inductive-bias-driven Reinforcement Learning for Efficient Schedules in Heterogeneous Clusters.”  Proceedings of the 37th International Conference on Machine Learning, Online, PMLR 119, 2020. (
  • Subho S. Banerjee, Zbigniew T. Kalbarczyk, and Ravishankar K. Iyer. 2019. AcMC²: Accelerating Markov Chain Monte Carlo Algorithms for Probabilistic Models. In Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems(ASPLOS ’19). Association for Computing Machinery, New York, NY, USA, 515–528. DOI: