Determining application-specific peak power and energy requirements for ultra-low power processors

Hari Cherupalli, Henry Duwe, Weidong Ye, Rakesh Kumar, John Sartori

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Many emerging applications such as IoT, wearables, implantables, and sensor networks are power-and energy-constrained. These applications rely on ultra-low-power processors that have rapidly become the most abundant type of processor manufactured today. In the ultra-low-power embedded systems used by these applications, peak power and energy requirements are the primary factors that determine critical system characteristics, such as size, weight, cost, and lifetime. While the power and energy requirements of these systems tend to be application-specific, conventional techniques for rating peak power and energy cannot accurately bound the power and energy requirements of an application running on a processor, leading to over-provisioning that increases system size and weight. In this paper, we present an automated technique that performs hardware-software co-analysis of the application and ultra-low-power processor in an embedded system to determine application-specific peak power and energy requirements. Our technique provides more accurate, tighter bounds than conventional techniques for determining peak power and energy requirements, reporting 15% lower peak power and 17% lower peak energy, on average, than a conventional approach based on profiling and guardbanding. Compared to an aggressive stressmark-based approach, our technique reports power and energy bounds that are 26% and 26% lower, respectively, on average. Also, unlike conventional approaches, our technique reports guaranteed bounds on peak power and energy independent of an application's input set. Tighter bounds on peak power and energy can be exploited to reduce system size, weight, and cost.

Original languageEnglish (US)
Pages (from-to)3-16
Number of pages14
JournalACM SIGPLAN Notices
Issue number4
StatePublished - Apr 4 2017

Bibliographical note

Funding Information:
9. Acknowledgments This work was supported in part by NSF, SRC, and CFAR, within STARnet, a Semiconductor Research Corporation program sponsored by MARCO and DARPA. The authors would like to thank anonymous reviewers and Professor Lizy John for their suggestions and feedback.

Publisher Copyright:
© 2017 ACM.


  • Embedded computing
  • Energy management
  • Internet of things (iot)
  • Power


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