A Retargetable Compilation Methodology for Embedded Digital Signal Processors Using a Machine-Dependent Code Optimization Library

Abstract

We address the problem of code generation for embedded DSP systems. Such systems devote a limited quantity of silicon to program memory, so the embedded software must be sufficiently dense. Additionally, this software must be written so as to meet various high-performance constraints. Unfortunately, current compiler technology is unable to generate dense, high-performance code for DSPs, due to the fact that it does not provide adequate support for the specialized architectural features of DSPs via machine-dependent code optimizations. Thus, designers often program the embedded software in assembly, a very time-consuming task. In order to increase productivity, compilers must be developed that are capable of generating high-quality code for DSPs. The compilation process must also be made retargetable, so that a variety of DSPs may be efficiently evaluated for potential use in an embedded system. We present a retargetable compilation methodology that enables high-quality code to be generated for a wide range of DSPs. Previous work in retargetable DSP compilation has focused on complete automation, and this desire for automation has limited the number of machine-dependent optimizations that can be supported. In our efforts, we have given code quality higher priority over completed automation. We demonstrate how by using a library of machine-dependent optimization routines accessible via a programming interface, it is possible to support a wide range of machine-dependent optimizations, albeit at some cost to automation. Experimental results demonstrate the effectiveness of our methodology, which has been uses to build good-quality compilers for three fixed-point DSPs.