This work presents MLIR, a novel approach to building reusable and extensible compiler
infrastructure. MLIR addresses software fragmentation, compilation for heterogeneous …

This work presents MLIR, a novel approach to building reusable and extensible compiler
infrastructure. MLIR aims to address software fragmentation, improve compilation for …

We present Polygeist, a new compilation flow that connects the MLIR compiler infrastructure
to cutting edge polyhedral optimization tools. It consists of a C and C++ frontend capable of …

Because of the increasing demand for intensive computation in deep neural networks,
researchers have developed both hardware and software mechanisms to reduce the …

Multi-level intermediate representations (IR) show great promise for lowering the design
costs for domain-specific compilers by providing a reusable, extensible, and non-opini …

Well-crafted libraries deliver much higher performance than code generated by
sophisticated application programmers using advanced optimizing compilers. When a code …

Computation in-memory is a promising non-von Neumann approach aiming at completely
diminishing the data transfer to and from the memory subsystem. Although a lot of …

A typical compiler flow relies on a uni-directional sequence of translation/optimization steps
that lower the program abstract representation, making it hard to preserve higher-level …

Memristor-based processing-in-memory architecture is a promising solution to the memory
bottleneck in the neural network (NN) processing. A major challenge for the programmability …

We present PET-to-MLIR, a new tool to enter the MLIR compiler framework from C source.
The tool is based on the popular PET and ISL libraries for extracting and manipulating quasi …