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Neural Network-based Prediction Algorithms for In-Door Multi-Source Energy Harvesting System for Non-Volatile Processors

Published: 18 May 2016 Publication History

Abstract

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.

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  • (2023)Enhancement of Energy Efficiency Using Ai Techniques: Systematic Literature ReviewSSRN Electronic Journal10.2139/ssrn.4473009Online publication date: 2023
  • (2018)Prediction-based fast thermoelectric generator reconfiguration for energy harvesting from vehicle radiators2018 Design, Automation & Test in Europe Conference & Exhibition (DATE)10.23919/DATE.2018.8342130(877-880)Online publication date: Mar-2018
  • (2017)Multisource Indoor Energy Harvesting for Nonvolatile ProcessorsIEEE Design & Test10.1109/MDAT.2017.268224234:3(42-49)Online publication date: Jun-2017
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  1. Neural Network-based Prediction Algorithms for In-Door Multi-Source Energy Harvesting System for Non-Volatile Processors

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    cover image ACM Conferences
    GLSVLSI '16: Proceedings of the 26th edition on Great Lakes Symposium on VLSI
    May 2016
    462 pages
    ISBN:9781450342742
    DOI:10.1145/2902961
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 18 May 2016

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    Author Tags

    1. energy harvesting
    2. multiple energy source
    3. neural network
    4. non-volatile processors

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    GLSVLSI '16: Great Lakes Symposium on VLSI 2016
    May 18 - 20, 2016
    Massachusetts, Boston, USA

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    GLSVLSI '16 Paper Acceptance Rate 50 of 197 submissions, 25%;
    Overall Acceptance Rate 312 of 1,156 submissions, 27%

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    Cited By

    View all
    • (2023)Enhancement of Energy Efficiency Using Ai Techniques: Systematic Literature ReviewSSRN Electronic Journal10.2139/ssrn.4473009Online publication date: 2023
    • (2018)Prediction-based fast thermoelectric generator reconfiguration for energy harvesting from vehicle radiators2018 Design, Automation & Test in Europe Conference & Exhibition (DATE)10.23919/DATE.2018.8342130(877-880)Online publication date: Mar-2018
    • (2017)Multisource Indoor Energy Harvesting for Nonvolatile ProcessorsIEEE Design & Test10.1109/MDAT.2017.268224234:3(42-49)Online publication date: Jun-2017
    • (2017)Algorithm accelerations for luminescent solar concentrator-enhanced reconfigurable onboard photovoltaic system2017 22nd Asia and South Pacific Design Automation Conference (ASP-DAC)10.1109/ASPDAC.2017.7858342(318-323)Online publication date: Jan-2017
    • (2016)Dynamic converter reconfiguration for near-threshold non-volatile processors using in-door energy harvesting2016 IEEE 34th International Conference on Computer Design (ICCD)10.1109/ICCD.2016.7753292(289-295)Online publication date: Oct-2016

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