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Signal-Processing and Data-Compression on Beaglebone Black used as embedded HPC-performance-monitoring device

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Short Description

In recent years, energy-efficiency is becoming a key challenge in the High Performance Computing (HPC) domain. Indeed, while the demand for more powerful supercomputers is constantly increasing, integrated computing architectures are facing power/thermal challenges that are limiting the performance benefits of technology scaling. However, even if energy-efficiency is of primary interest, such infrastructures have a limited introspection on the measurement of power and energy dissipated at run-time. Aiming at bridging this gap, we designed and developed at IIS, in collaboration with some Commercial Partners, a scalable and highly accurate power-consumption monitoring framework, based on a state-of-the-art embedded monitoring device (namely a Beaglebone Black) and a power-sensing-board placed at the node power-supply.

Goal of this Project → Evaluation of different compression and spectral information extraction strategies (based on open-source ARM-Linux loss-less implementations when available) for the best performance on our application-scenario.

Status: Available

Looking for 1 Master Students (Semester Project)
Supervision: Antonio Libri, Andrea Bartolini


  • Good knowledge on signal processing and C programming.
  • Experience using laboratory instrumentation - signal generators, oscilloscopes, Matlab, etc.
  • Experience on microcontrollers would be an asset.


40% Theory
40% Implementation (C programming for embedded systems)
20% Testing


Luca Benini

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Detailed Task Description


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