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197734 https://hdl.handle.net/2286/R.2.N.197734 http://rightsstatements.org/vocab/InC/1.0/ All Rights Reserved 2024 34 pages Masters Thesis Academic theses Text eng Kumar, Abhinav Shrivastava, Aviral Chakrabarti, Chaitali SO, Hwisoo Arizona State University Partial requirement for: M.S., Arizona State University, 2024 Field of study: Computer Science Traditional Digital Signal Processing (DSP) compilers operate at a low level, such as C and assembly. Lowering from a high-level representation to low-level ones often results in a loss of high-level information, which is critical for optimizations based on DSP-specific theorems and properties. Consequently, traditional DSP compilers miss many optimization opportunities available at a high level. The MLIR (Multi-level Intermediate Representation) framework, an emerging multi-level compiler infras tructure, supports specifying optimizations at a higher level. This thesis introduces a DSP Dialect within the MLIR framework to perform domain-specific optimizations based on DSP theorems and properties at a high level. Additionally, a domain-specific language (DSL) is proposed to facilitate the development of DSP applications. Ex perimental results demonstrate that domain-specific optimizations of the DSP dialect can improve the execution time of DSP applications by up to 10x compared to im plementations in C and affine levels. Computer Science Electrical Engineering Computer Engineering DSP-MLIR: A Compiler for Digital Signal Processing in MLIR