Training deep learning models is compute-intensive and there is an industry-wide trend
towards hardware specialization to improve performance. To systematically benchmark …

Machine-Learning tasks are becoming pervasive in a broad range of domains, and in a
broad range of systems (from embedded systems to data centers). At the same time, a small …

This paper describes a learning-based approach to the acceleration of approximate
programs. We describe the Parrot transformation, a program transformation that selects and …

Approximate computing is claimed to be a powerful knob for alleviating the peak power and
energy-efficiency issues. However, providing a consistent benchmark suit with diverse …

Machine Learning (ML) tasks are becoming pervasive in a broad range of applications, and
in a broad range of systems (from embedded systems to data centers). As computer …

Deep learning has been popularized by its recent successes on challenging artificial
intelligence problems. One of the reasons for its dominance is also an ongoing challenge …

Real-time predictive modelling with desired accuracy is highly expected in industrial artificial
intelligence (IAI), where neural networks play a key role. Neural networks in IAI require …

As improvements in per-transistor speed and energy efficiency diminish, radical departures
from conventional approaches are becoming critical to improving the performance and …

Many applications that can take advantage of accelerators are amenable to approximate
execution. Past work has shown that neural acceleration is a viable way to accelerate …

Approximate computing trades off accuracy for better performance and energy efficiency. It
offers promising optimization opportunities for a wide variety of modern applications, from …