Neural network-based prediction algorithms for in-door multi-source energy harvesting system for non-volatile processors

Ning Liu, Caiwen Ding, Yanzhi Wang, Jingtong Hu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


Due to size, longevity, safety, and recharging concerns, energy harvesting is becoming a better choice for many wearable embedded systems than batteries. However, harvested energy is intrinsically unstable. In order to overcome this drawback, non-volatile processors (NVPs) have been proposed to bridge intermittent program execution. However, even with NVPs, frequent power interruptions will severely degrade system performance. Hence, in this paper we adopt a multi-source in-door energy harvesting architecture to compensate the shortcoming of single energy source. We further investigate power harvesting prediction techniques, which are critical for NVP systems since they can coordinate with task scheduler in the NVP system to compensate the intermittent ambient energy harvesting. We investigate prediction methods both for single energy harvesting source and for multiple energy harvesting sources, the total output power of which is more stable compared with the single source case. A comprehensive evaluation framework has been developed using actually measured harvesting traces on the proposed neural network-based power harvesting prediction methods. It turns out that the most favorable prediction methods are directly predicting the total output power of DC-DC converters (connecting between energy sources and NVP), or predicting the total input power of DC-DC converters first and then inferring the total output power using a learned mapping function, for multi-source power harvesting predictions.

Original languageEnglish (US)
Title of host publicationGLSVLSI 2016 - Proceedings of the 2016 ACM Great Lakes Symposium on VLSI
PublisherAssociation for Computing Machinery
Number of pages6
ISBN (Electronic)9781450342742
StatePublished - May 18 2016
Event26th ACM Great Lakes Symposium on VLSI, GLSVLSI 2016 - Boston, United States
Duration: May 18 2016May 20 2016


Other26th ACM Great Lakes Symposium on VLSI, GLSVLSI 2016
CountryUnited States


  • Energy harvesting
  • Multiple energy source
  • Neural network
  • Non-volatile processors

ASJC Scopus subject areas

  • Engineering(all)

Fingerprint Dive into the research topics of 'Neural network-based prediction algorithms for in-door multi-source energy harvesting system for non-volatile processors'. Together they form a unique fingerprint.

Cite this