AI 中文总结
研究处理图像等所需的大型FIR滤波器快速近似问题，统一多速率等技术为原语，在单一设计语言中自动搜索程序空间，用梯度下降拟合参数生成帕累托前沿，生成高质量快速滤波器近似并能转为优化C++代码。
AI 中文摘要
处理图像、视频和音频通常需要运行具有严格性能和延迟要求的大型有限脉冲响应（FIR）滤波器。先前快速滤波器近似方法是一些关键技术的特殊情况或组合，如多速率和循环滤波，以及将滤波器分解为和或级联。我们将这些技术统一为用于快速一维和二维滤波器的单一设计语言中的原语。给定目标滤波器进行近似，我们自动搜索此程序空间，用梯度下降拟合连续参数，以生成在性能和质量之间权衡的算法帕累托前沿。我们的系统为几种流行的成像和音频滤波器生成的滤波器近似在质量和速度上都远超先前描述的。此外，我们展示了如何将此设计空间中的程序自动降低为针对数据局部性进行融合的优化、向量化、并行C++代码。
英文摘要
Processing images, video, and audio often requires running large finite impulse response (FIR) filters with strict performance and latency requirements. Prior methods for fast filter approximations are special cases or combinations of a few key techniques: multi-rate and recurrent filtering, and decomposing filters into sums or cascades. We unify these techniques as primitives within a single design language for fast 1D and 2D filters. Given a target filter to approximate, we automatically search this program space, fitting continuous parameters with gradient descent, to generate a Pareto frontier of algorithms that trade off performance with quality. Our system produces substantially higher-quality and faster filter approximations than have been previously described for several popular imaging and audio filters. Furthermore we demonstrate how to automatically lower programs in this design space to optimized, vectorized, parallel, C++ code which is fused for data locality.
