This article aims to tackle two fundamental memory bottlenecks: limited off-chip bandwidth
(bandwidth wall) and long access latency (memory wall). To achieve this goal, our approach …

The rise of machine learning methods on heavily resource constrained devices requires not
only the choice of a suitable model architecture for the target platform, but also the …

Python has become an increasingly popular programming language, especially in the areas
of data analytics and machine learning. Many modern Python packages employ a multi …

Executing machine learning workloads locally on resource constrained microcontrollers
(MCUs) promises to drastically expand the application space of IoT. However, so-called …

Modern software-based systems are highly configurable and come with a number of
configuration options that impact the performance of the systems. However, selecting …

Many energy-constrained cyber-physical systems require both timeliness and the execution
of tasks within given energy budgets. That is, besides knowledge on worst-case execution …

We present Phronesis, a learning framework for efficiently modeling the performance of data
analytic workloads as a function of their high-dimensional software configuration …

A large portion of current cloud and edge workloads feature Machine Learning (ML) tasks,
thereby requiring a deep understanding of their energy efficiency. While the holy grail for …

With the recent advances in both machine learning and embedded systems research, the
demand to deploy computational models for real-time execution on edge devices has …

A common aspect of today's cyber-physical systems is that multiple optimization-based
control tasks may execute in a shared processor. Such control tasks make use of online …