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An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...
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Speeding up Smartphone-Based Dew Computing: In Vivo Experiments Setup Via an Evolutionary Algorithm.

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Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
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Exploring Smartphone-Based Edge AI Inferences Using Real Testbeds.

Matías Hirsch1, Cristian Mateos1, Tim A Majchrzak2,3

  • 1ISISTAN (UNICEN-CONICET), Tandil 7000, Buenos Aires, Argentina.

Sensors (Basel, Switzerland)
|May 14, 2025
PubMed
Summary
This summary is machine-generated.

Smartphone clusters offer a competitive edge for AI tasks, providing valuable computing power for real-time applications. This approach enhances edge AI capabilities, especially for computer vision, without heavy cloud reliance.

Keywords:
cluster computingedge AIenergy efficiencysmartphones

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Area of Science:

  • Edge Artificial Intelligence (AI)
  • Computer Vision (CV)
  • Mobile Computing

Background:

  • Edge AI is expanding due to accessible pre-trained models and AI frameworks.
  • Deep learning (DL) models are crucial for real-time computer vision tasks like object recognition.
  • Existing edge AI platforms often rely on cloud resources or homogeneous Single-Board Computers (SBCs), with limited exploration of nomadic hardware like smartphones.

Purpose of the Study:

  • To investigate the competitiveness of smartphone-based edge AI for real-time computer vision inferences.
  • To compare the performance of smartphone clusters against SBCs for edge AI workloads.
  • To evaluate the impact of edge AI tasks on smartphone battery life.

Main Methods:

  • Utilized three pre-trained DL models for computer vision tasks.
  • Employed eight heterogeneous edge nodes: five low/mid-end smartphones and three SBCs.
  • Conducted experiments using a toolset for battery-driven edge computing tests across three image stream processing scenarios.

Main Results:

  • Compared latency and energy efficiency between smartphone clusters and SBC-only configurations.
  • Measured the effect of workload execution on smartphone battery levels in battery-driven settings.
  • Demonstrated that smartphone clusters can provide significant computing resources for edge AI.

Conclusions:

  • Edge AI leveraging smartphone clusters is a viable and competitive approach for real-time performance.
  • Smartphone clusters can augment edge AI capabilities, supporting its expansion into diverse application scenarios.
  • The study provides empirical evidence for the utility of smartphones as edge AI nodes.