nelli: a lightweight frontend for MLIR

Multi-Level Intermediate Representation (MLIR) is a novel compiler infrastructure that aims to provide modular and extensible components to facilitate building domain specific compilers. However, since MLIR models programs at an intermediate level of abstraction, and most extant frontends are at a very high level of abstraction, the semantics and mechanics of the fundamental transformations available in MLIR are difficult to investigate and employ in and of themselves. To address these challenges, we have developed \texttt{nelli}, a lightweight, Python-embedded, domain-specific, language for generating MLIR code. \texttt{nelli} leverages existing MLIR infrastructure to develop Pythonic syntax and semantics for various MLIR features. We describe \texttt{nelli}'s design goals, discuss key details of our implementation, and demonstrate how \texttt{nelli} enables easily defining and lowering compute kernels to diverse hardware platforms

Parameterized validation of UML-Like models for reactive embedded systems

Ph.DDOCTOR OF PHILOSOPH

Introducing Reference Semantics via Refinement

Two types of semantics have been given to object-oriented formal specification languages. Value semantics denote a class by a set of values representing its objects. Reference semantics denote a class by a set of references, or pointers, to values representing its objects. While adopting the former facilitates formal reasoning, adopting the latter facilitates transformation to object-oriented code. In this paper, we propose a combined approach using value semantics for abstract specification and reasoning, and then refining to a reference semantics before transforming specification to code