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An algorithm for trading off quantization error with hardware resources for MATLAB-based FPGA design

机译:用于基于MATLAB的FPGA设计和硬件资源之间权衡量化误差的算法

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Most practical FPGA designs of digital signal processing (DSP) applications are limited to fixed-point arithmetic owing to the cost and complexity of floating-point hardware. While mapping DSP applications onto FPGAs, a DSP algorithm designer must determine the dynamic range and desired precision of input, intermediate, and output signals in a design implementation. The first step in a MATLAB-based hardware design flow is the conversion of the floating-point MATLAB code into a fixed-point version using "quantizers" from the filter design and analysis (FDA) toolbox for MATLAB. This paper describes an approach to automate the conversion of floating-point MATLAB programs into fixed-point MATLAB programs, for mapping to FPGAs by profiling the expected inputs to estimate errors. Our algorithm attempts to minimize the hardware resources while constraining the quantization error within a specified limit. Experimental results on five MATLAB benchmarks are reported for Xilinx Virtex II FPGAs.
机译:由于浮点硬件的成本和复杂性,数字信号处理(DSP)应用的大多数实际FPGA设计都限于定点算法。在将DSP应用映射到FPGA时,DSP算法设计人员必须在设计实现中确定输入,中间和输出信号的动态范围和所需的精度。基于MATLAB的硬件设计流程的第一步是使用MATLAB的滤波器设计和分析(FDA)工具箱中的“量化器”将浮点MATLAB代码转换为定点版本。本文介绍了一种自动将浮点MATLAB程序转换为定点MATLAB程序的方法,该方法可通过对期望的输入进行概要分析来估计误差,从而映射到FPGA。我们的算法尝试将硬件资源最小化,同时将量化误差限制在指定的范围内。报告了Xilinx Virtex II FPGA在五个MATLAB基准上的实验结果。

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