基于内存密集型c内核的自动FPGA合成

Matthew Milford, J. McAllister
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引用次数: 3

摘要

在现代FPGA上实现高性能图像和信号处理应用是一个具有挑战性的实现问题,因为这些系统中有大量的数据帧流。具体来说,为了满足这些应用的高带宽和数据存储需求,必须在RTL (Register Transfer Level)手动指定复杂的分层内存架构。将高级操作描述(例如以C程序的形式)转换为FPGA架构的自动化方法无法自动实现这种架构。本文提出了一种解决这一问题的方法。它提供了一个编译器来自动地从C程序派生这种内存体系结构。通过将输入C程序转换为一种独特的数据流建模方言,称为有值数据流(VDF),可以利用为该方言开发的映射和综合方法来自动创建高性能图像和视频处理架构。用于运动估计(CIF帧速度为30 fps)、矩阵乘法(128×128 @ 500 iter/秒)和索贝尔边缘检测(720p @ 30 fps)的内存密集型C核,这些都是当前最先进的基于C的合成工具无法实现的,它们自动从算法的C描述中衍生出来。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatic FPGA synthesis of memory intensive C-based kernels
Realising high performance image and signal processing applications on modern FPGA presents a challenging implementation problem due to the large data frames streaming through these systems. Specifically, to meet the high bandwidth and data storage demands of these applications, complex hierarchical memory architectures must be manually specified at the Register Transfer Level (RTL). Automated approaches which convert high-level operation descriptions, for instance in the form of C programs, to an FPGA architecture, are unable to automatically realise such architectures. This paper presents a solution to this problem. It presents a compiler to automatically derive such memory architectures from a C program. By transforming the input C program to a unique dataflow modelling dialect, known as Valved Dataflow (VDF), a mapping and synthesis approach developed for this dialect can be exploited to automatically create high performance image and video processing architectures. Memory intensive C kernels for Motion Estimation (CIF Frames at 30 fps), Matrix Multiplication (128×128 @ 500 iter/sec) and Sobel Edge Detection (720p @ 30 fps), which are unrealisable by current state-of-the-art C-based synthesis tools, are automatically derived from a C description of the algorithm.
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