2019

  • [C55] R. Zhao, Y. Hu, J. Dotzel, C. De Sa, and Z. Zhang, Building Efficient Deep Neural Networks with Unitary Group Convolutions, to appear in The Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2019. [preprint]
  • [C54] Y. Zhou, H. Ren, Y. Zhang, B. Keller, B. Khailany, and Z. Zhang, PRIMAL: Power Inference using Machine Learning, to appear in Design Automation Conference (DAC), Jun. 2019.
  • [C53] C. Yu and Z. Zhang, Painting on Placement: Forecasting Routing Congestion using Conditional Generative Adversarial Nets, to appear in Design Automation Conference (DAC), Jun. 2019.
  • [C52] Z. Jiang, H. Jin, G. E. Suh, and Z. Zhang, Designing Secure Cryptographic Accelerators with Information Flow Enforcement: A Case Study on AES, to appear in Design Automation Conference (DAC), Jun. 2019.
  • [C51] S. Dai and Z. Zhang, Improving Scalability of Exact Modulo Scheduling with Specialized Conflict-Driven Learning, to appear in Design Automation Conference (DAC), Jun. 2019.
  • [C50] G. Liu, J. Primmer, and Z. Zhang, Rapid Generation of High-Quality RISC-V Processors from Functional Instruction Set Specifications, to appear in Design Automation Conference (DAC), Jun. 2019.
  • [C49] N. Srivastava, H. Rong, P. Barua, G. Feng, H. Cao, Z. Zhang, D. Albonesi, V. Sarkar, W. Chen, P. Petersen, G. Lowney, A. Herr, C. Hughes, T. Mattson, and P. Dubey, T2S-Tensor: Productively Generating High-Performance Spatial Hardware for Dense Tensor Computations, to appear in International Symposium on Field-Programmable Custom Computing Machines (FCCM), Apr./May 2019.
  • [C48] E. Ustun, S. Xiang, J. Gui, C. Yu, and Z. Zhang, LAMDA: Learning-Assisted Multi-Stage Autotuning for FPGA Design Closure, to appear in International Symposium on Field-Programmable Custom Computing Machines (FCCM), Apr./May 2019.
  • [C47] Y.-H. Lai, Y. Chi, Y. Hu, J. Wang, C. H. Yu, Y. Zhou, J. Cong, and Z. Zhang, HeteroCL: A Multi-Paradigm Programming Infrastructure for Software-Defined Reconfigurable Computing, International Symposium on Field-Programmable Gate Arrays (FPGA), Feb. 2019. (Best Paper Award)
  • [A3] R. Zhao, Y. Hu, J. Dotzel, C. De Sa, and Z. Zhang, Improving Neural Network Quantization using Outlier Channel Splitting, arXiv e-print, arXiv:1901.09504, Jan. 2019.
  • [J9] G. Liu and Z. Zhang, PIMap: A Flexible Framework for Improving LUT-Based Technology Mapping via Parallelized Iterative Optimization, ACM Transactions on Reconfigurable Technology and Systems (TRETS), Jan. 2019.

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