Customizable Composition and Parameterization of Hardware Design Transformations

Abstract

A promising approach to high-level design is to start initially with an obvious but possibly inefficient design, and apply multiple transformations to meet design goals. Many hardware compilation tools support a fixed recipe of applying design transformations, but designers have few options to adapt the recipe without re-writing the tools themselves. In addition, complex transformations based on linear programming and geometric programming are often not included. This paper proposes anew approach that enables designers to customize the composition and parameterization of different types of design transformations in a unified framework, using a high-level language to control a transformation engine to automate the application of design transformations. Our approach is implemented by a tool based on the Python language and the ROSE compiler framework, which supports both syntax-directed transformations such as loop coalescing, and goal-directed transformations such as geometric programming. We illustrate how customizing the composition and parameterization of design transformations can lead to designs with different trade-offs in performance, resource usage, and energy efficiency. We evaluate our approach on benchmarks including matrix multiplication, Monte Carlo simulation of Asian options, edge detection, FIR filtering, and motion estimation.