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 …

Polyhedral techniques have been widely used for automatic code optimization in low-level
compilers and higher-level processes. Loop optimization is central to this technique, and …

Program synthesis is an active research field in academia, national labs, and industry. Yet,
work directly applicable to scientific computing, while having some impressive successes …

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 …

Loop tiling is a key high-level transformation which is known to maximize locality in loop
intensive programs. It has been successfully applied to a number of applications including …

The future of computation is massively parallel and heterogeneous with specialized
accelerator devices and instruction sets in both edge-and cluster-computing. However …

Most quantum compiler transformations and qubit allocation techniques to date are either
peep-hole focused or rely on sliding windows that depend on a number of external …

Many computational problems consider memory throughput a performance bottleneck,
especially in the domain of parallel computing. Software needs to be attuned to hardware …

The strong demand for efficient and performant deployment of Deep Learning (DL)
applications prompts the rapid development of a rich DL ecosystem. To keep up with this fast …

Optimizing compilers for efficient machine learning are more important than ever due to the
rising ubiquity of the application domain in numerous facets of life. Predictive model-guided …