Since the mid-1990s, researchers have been trying to use machine-learning-based
approaches to solve a number of different compiler optimization problems. These …

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 …

In the last decade, machine-learning-based compilation has moved from an obscure
research niche to a mainstream activity. In this paper, we describe the relationship between …

We present the design and implementation of an automatic polyhedral source-to-source
transformation framework that can optimize regular programs (sequences of possibly …

Heterogeneous multiprocessors are increasingly important in the multi-core era due to their
potential for high performance and energy efficiency. In order for software to fully realize this …

The polyhedral model is a powerful framework for automatic optimization and parallelization.
It is based on an algebraic representation of programs, allowing to construct and search for …

Automatic systolic array generation has long been an interesting topic due to the need to
reduce the lengthy development cycles of manual designs. Existing automatic systolic array …

The polyhedral model provides powerful abstractions to optimize loop nests with regular
accesses. Affine transformations in this model capture a complex sequence of execution …

We present the design and implementation of a fully automatic polyhedral source-to-source
transformation framework that can optimize regular programs (sequences of possibly …

Compiler-based auto-parallelization is a much studied area, yet has still not found wide-
spread application. This is largely due to the poor exploitation of application parallelism …