High-quality compilation of Digital Signal Processing (DSP) algorithms is crucial for achieving real-time performance and optimizing resource utilization.
Traditional compilers often struggle to effectively optimize DSP applications since their optimization passes mainly deal with low-level intermediate representations.
This paper introduces DSP-MLIR – a comprehensive framework for DSP application development and optimization.
DSP-MLIR comprises i) a Python-like domain-specific language (DSL) (named DSP-DSL) for intuitive and easier programming of DSP applications, ii) a dedicated MLIR dialect (named DSP-dialect) with 90+ operations and 16 optimizations at the level of DSP operations, and iii) lowerings to the Affine and standard MLIR dialects for high-quality compilation flow for DSP applications.
The effectiveness of the proposed DSP-MLIR is evaluated by comparing the runtimes of the binaries generated by the various compilation flows, including GCC, Clang, Hexagon-Clang, and existing MLIR passes.
Experiments on 20 DSP applications collected from various sources demonstrate an average performance improvement of 12% over state-of-the-art compilation flows with a 10% reduction in the generated binary size and no significant variation in compilation time.
Further, expressing DSP applications in the proposed DSP-DSL reduces the code complexity and development time of DSP applications (as measured in lines of code (LOC)) by an average of 5x over their specification in the programming language, ``C''.

The DSP-MLIR framework is open-source and available at:~\url{https://github.com/MPSLab-ASU/DSP_MLIR}