FPGA-Based Hardware Acceleration of Lithographic Aerial Image Simulation

被引:21
|
作者
Cong, Jason [1 ]
Zou, Yi [1 ]
机构
[1] Univ Calif Los Angeles, Dept Comp Sci, Los Angeles, CA 90095 USA
关键词
Algorithms; Performance; Design; Lithography simulation; coprocessor acceleration; FPGA; DESIGN;
D O I
10.1145/1575774.1575776
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Lithography simulation, an essential step in design for manufacturability (DFM), is still far from computationally efficient. Most leading companies use large clusters of server computers to achieve acceptable turn-around time. Thus coprocessor acceleration is very attractive for obtaining increased computational performance with a reduced power consumption. This article describes the implementation of a customized accelerator on FPGA using a polygon-based simulation model. An application-specific memory partitioning scheme is designed to meet the bandwidth requirements for a large number of processing elements. Deep loop pipelining and ping-pong buffer based function block pipelining are also implemented in our design. Initial results show a 15X speedup versus the software implementation running on a microprocessor, and more speedup is expected via further performance tuning. The implementation also leverages state-of-art C-to-RTL synthesis tools. At the same time, we also identify the need for manual architecture-level exploration for parallel implementations. Moreover, we implement the algorithm on NVIDIA GPUs using the CUDA programming environment, and provide some useful comparisons for different kinds of accelerators.
引用
收藏
页数:29
相关论文
共 50 条
  • [21] An FPGA-Based Accelerator for LambdaRank in Web Search Engines
    Yan, Jing
    Xu, Ning-Yi
    Cai, Xiong-Fei
    Gao, Rui
    Wang, Yu
    Luo, Rong
    Hsu, Feng-Hsiung
    ACM TRANSACTIONS ON RECONFIGURABLE TECHNOLOGY AND SYSTEMS, 2011, 4 (03)
  • [22] FPGA-NHAP: A General FPGA-Based Neuromorphic Hardware Acceleration Platform With High Speed and Low Power
    Liu, Yijun
    Chen, Yuehai
    Ye, Wujian
    Gui, Yu
    IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS, 2022, 69 (06) : 2553 - 2566
  • [23] Design and evaluation of a hardware/software FPGA-based system for fast image processing
    Kalomiros, J. A.
    Lygouras, J.
    MICROPROCESSORS AND MICROSYSTEMS, 2008, 32 (02) : 95 - 106
  • [24] A Fast and Efficient FPGA-based Level Set Hardware Accelerator for Image Segmentation
    Liu Ye
    Xiao Jianbiao
    Wu Fei
    Chang Liang
    Zhou Jun
    JOURNAL OF ELECTRONICS & INFORMATION TECHNOLOGY, 2021, 43 (06) : 1525 - 1532
  • [25] FPGA-based hardware accelerator for SENSE (a parallel MR image reconstruction method)
    Inam, Omair
    Basit, Abdul
    Qureshi, Mahmood
    Omer, Hammad
    COMPUTERS IN BIOLOGY AND MEDICINE, 2020, 117
  • [26] FPGA-based Hardware-in-the-Loop Simulation of a Rectifier with Power Factor Correction
    Kiffe, Axel
    Schulte, Thomas
    2015 17TH EUROPEAN CONFERENCE ON POWER ELECTRONICS AND APPLICATIONS (EPE'15 ECCE-EUROPE), 2015,
  • [27] ARC 2014: A Multidimensional FPGA-Based Parallel DBSCAN Architecture
    Scicluna, Neil
    Bouganis, Christos-Savvas
    ACM TRANSACTIONS ON RECONFIGURABLE TECHNOLOGY AND SYSTEMS, 2015, 9 (01)
  • [28] An FPGA-based Hardware Accelerator for Iris Segmentation
    Avey, Joe
    Jones, Phillip
    Zambreno, Joseph
    2018 INTERNATIONAL CONFERENCE ON RECONFIGURABLE COMPUTING AND FPGAS (RECONFIG), 2018,
  • [29] Protocol Aware ATE with FPGA-based Hardware
    Aggarwal, Vineet
    2008 IEEE AUTOTESTCON, VOLS 1 AND 2, 2008, : 322 - 324
  • [30] FPGA-based hardware to achieve the stereoscopic display
    Zhang Guangwei
    An Zhiyong
    Zheng Fangyin
    Zhang Weiwei
    ISTM/2007: 7TH INTERNATIONAL SYMPOSIUM ON TEST AND MEASUREMENT, VOLS 1-7, CONFERENCE PROCEEDINGS, 2007, : 1732 - 1735