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

Accurate automatic optimization heuristics are necessary for dealing with thecomplexity and
diversity of modern hardware and software. Machine learning is aproven technique for …

On-device deep learning (DL) inference has attracted vast interest. Mobile CPUs are the
most common hardware for on-device inference and many inference frameworks have been …

Predictive modeling using machine learning is an effective method for building compiler
heuristics, but there is a shortage of benchmarks. Typical machine learning experiments …

Since performance is not portable between platforms, engineers must fine-tune heuristics for
each processor in turn. This is such a laborious task that high-profile compilers, supporting …

Mobile GPU, as a ubiquitous and powerful accelerator, plays an important role in
accelerating on-device DNN (Deep Neural Network) inference. The frequent-upgrade and …

Heterogeneous computing, combining devices with different architectures such as CPUs
and GPUs, is rising in popularity and promises increased performance combined with …

Microscopic traffic simulation is associated with substantial runtimes, limiting the feasibility of
large-scale evaluation of traffic scenarios. Even though today heterogeneous hardware …

Mobile GPU, as the ubiquitous computing hardware on almost every smartphone, is being
exploited for the deep learning inference. In this paper, we present our measurements on …

Constructing compilers is hard. Optimising compilers are multi-million dollar projects
spanning years of development, yet remain unable to fully exploit the available performance …