Deep Neural Networks (DNNs) have emerged as the core enabler of many major
applications on mobile devices. To achieve high accuracy, DNN models have become …

The polyhedral model for loop parallelization has proved to be an effective tool for advanced
optimization and automatic parallelization of programs in higher-level languages. Yet, to …

This paper presents the design and implementation of PolyMage, a domain-specific
language and compiler for image processing pipelines. An image processing pipeline can …

Existing tensor compilers have proven their effectiveness in deploying deep neural networks
on general-purpose hardware like CPU and GPU, but optimizing for neural processing units …

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 …

Most stencil computations allow tile-wise concurrent start, ie, there always exists a face of
the iteration space and a set of tiling hyperplanes such that all tiles along that face can be …

We present a new library for extracting a polyhedral model from C source. The library is
based on clang, the LLVM C frontend, and isl, a library for manipulating quasi-affine sets …

High-level loop transformations are a key instrument in mapping computational kernels to
effectively exploit the resources in modern processor architectures. Nevertheless, selecting …

Deep learning frameworks automate the deployment, distribution, synchronization, memory
allocation, and hardware acceleration of models represented as graphs of computational …

Optimizing compilers exploit the memory hierarchy using loop tiling and fusion, but these
two transformations usually interfere with each other due to the oversight of transformations …