We present a new algorithm to automatically schedule Halide programs for high-
performance image processing and deep learning. We significantly improve upon the …

Abstract Machine learning (ML) is increasingly seen as a viable approach for building
compiler optimization heuristics, but many ML methods cannot replicate even the simplest of …

Interest in applying Artificial Intelligence (AI) techniques to compiler optimizations is
increasing rapidly, but compiler research has a high entry barrier. Unlike in other domains …

Machine learning (ML) has become the de-facto approach for various scientific domains
such as computer vision and natural language processing. Despite recent breakthroughs …

One of the key challenges arising when compilers vectorize loops for today's SIMD-
compatible architectures is to decide if vectorization or interleaving is beneficial. Then, the …

We take initial steps in studying PAC-MDP algorithms with limited adaptivity, that is,
algorithms that change its exploration policy as infrequently as possible during regret …

A typical compiler such as GCC supports hundreds of optimizations controlled by
compilation flags for improving the runtime performance of the compiled program. Due to the …

The increasing complexity of computing systems places a tremendous burden on optimizing
compilers, requiring ever more accurate and aggressive optimizations. Machine learning …

Recent compilers offer a vast number of multilayered optimizations targeting different code
segments of an application. Choosing among these optimizations can significantly impact …

Fueled by recent accomplishments in quantum computing hardware and software, an
increasing number of problems from various application domains are being explored as …