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

In recent years, it has been witnessed that the systolic array is a successful architecture for
DNN hardware accelerators. However, the design of systolic arrays also encountered many …

Deep learning models with convolutional and recurrent networks are now ubiquitous and
analyze massive amounts of audio, image, video, text and graph data, with applications in …

High-level synthesis (HLS) is increasingly popular for the design of high-performance and
energy-efficient heterogeneous systems, shortening time-to-market and addressing today's …

This paper introduces Tiramisu, a polyhedral framework designed to generate high
performance code for multiple platforms including multicores, GPUs, and distributed …

While systolic array architectures have the potential to deliver tremendous performance, it is
notoriously challenging to customize an efficient systolic array processor for a target …

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 article addresses the compilation of a sequential program for parallel execution on a
modern GPU. To this end, we present a novel source-to-source compiler called PPCG …

Machine learning (ML) models are widely used in many important domains. For efficiently
processing these computational-and memory-intensive applications, tensors of these …

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 …