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

We present CrossSense, a novel system for scaling up WiFi sensing to new environments
and larger problems. To reduce the cost of sensing model training data collection …

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 …

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

We explore the novel application of Large Language Models to code optimization. We
present a 7B-parameter transformer model trained from scratch to optimize LLVM assembly …

Despite several attacks have been proposed, text-based CAPTCHAs are still being widely
used as a security mechanism. One of the reasons for the pervasive use of text captchas is …

The recent ground-breaking advances in deep learning networks (DNNs) make them
attractive for embedded systems. However, it can take a long time for DNNs to make an …

Air quality indicators and air quality index (AQI) prediction are effective approaches for urban
decision-makers, planners, managers and even city residents to arrange their risk …

As parallel applications become more complex, auto-tuning becomes more desirable,
challenging, and time-consuming. We propose, Bliss, a novel solution for auto-tuning …

On edge devices, data scarcity occurs as a common problem where transfer learning serves
as a widely-suggested remedy. Nevertheless, transfer learning imposes heavy computation …