A hardware-accelerated estimation-based power profiling unit - enabling early power-aware embedded software design and on-chip power management

Andreas Genser, Christian Bachmann, Christian Steger, Reinhold Weiss, Josef Haid

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in Buch/BerichtForschungBegutachtung

Abstract

The power consumption of battery powered and energy scavenging devices has become a major design metric for embedded systems. Increasingly complex software applications as well as rising demands in operating times, while having restricted power budgets are main drivers of power-aware system design as well as power management techniques. Within this work, a hardware-accelerated estimation-based power profiling unit delivering real-time power information has been developed. Power consumption feedback to the designer allows for real-time power analysis of embedded systems. Power saving potential as well as power-critical events can be identified in much less time compared to power simulations. Hence, the designer can take countermeasures already at early design stages, which enhances development efficiency and decreases time-to-market. Moreover, this work forms the basis for estimation-based on-chip power management by leveraging the power information for adoptions on system frequency and supply voltage in order to enhance the power efficiency of embedded systems. Power estimation accuracies achieved for a deep sub-micron smart-card controller are above 90% compared to gate-level simulations.

Originalspracheenglisch
TitelLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Herausgeber (Verlag)Springer-Verlag Italia
Seiten59-78
Seitenumfang20
DOIs
PublikationsstatusVeröffentlicht - 1 Jan 2019

Publikationsreihe

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band11225 LNCS
ISSN (Print)0302-9743
ISSN (elektronisch)1611-3349

    Fingerprint

ASJC Scopus subject areas

  • !!Theoretical Computer Science
  • !!Computer Science(all)

Dieses zitieren

Genser, A., Bachmann, C., Steger, C., Weiss, R., & Haid, J. (2019). A hardware-accelerated estimation-based power profiling unit - enabling early power-aware embedded software design and on-chip power management. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (S. 59-78). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 11225 LNCS). Springer-Verlag Italia. https://doi.org/10.1007/978-3-662-58834-5_4