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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
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Machine Learning on Mainstream Microcontrollers.

Fouad Sakr1, Francesco Bellotti1, Riccardo Berta1

  • 1Department of Electrical, Electronic and Telecommunication Engineering (DITEN)-University of Genoa, Via Opera Pia 11a, 16145 Genova, Italy.

Sensors (Basel, Switzerland)
|May 9, 2020
PubMed
Summary
This summary is machine-generated.

The Edge Learning Machine (ELM) framework enables machine learning on microcontrollers, achieving desktop performance on edge devices. Artificial Neural Networks (ANNs) generally outperform other algorithms like k-Nearest Neighbors (k-NN) and Decision Trees (DT).

Keywords:
ANNARMSTM32 NucleoSVMX-Cube-AIdecision treesedge analyticsedge computingembedded devicesk-NNmachine learning

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Area of Science:

  • Computer Science
  • Machine Learning
  • Embedded Systems

Background:

  • Edge devices require efficient machine learning for real-time data processing.
  • Existing frameworks often lack comprehensive algorithm comparison for diverse embedded platforms.
  • Optimizing machine learning performance on resource-constrained microcontrollers is a significant challenge.

Purpose of the Study:

  • To introduce the Edge Learning Machine (ELM), a novel framework for training machine learning models on desktops and deploying inferences on edge devices.
  • To evaluate and compare the performance of multiple supervised machine learning algorithms on various embedded platforms.
  • To address the gap in literature regarding the performance of different machine learning algorithms on microcontrollers.

Main Methods:

  • Implementation of Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), and Decision Tree (DT) algorithms in platform-independent C.
  • Utilization of STM32 X-Cube-AI for deploying Artificial Neural Networks (ANNs) on STM32 Nucleo boards.
  • Performance evaluation across six embedded boards and six datasets (four classification, two regression).

Main Results:

  • Edge devices achieved comparable performance and latency to desktop machines.
  • Artificial Neural Networks (ANNs) demonstrated superior performance in most scenarios.
  • k-Nearest Neighbors (k-NN) showed competitive results but with high memory demands, while Decision Trees (DT) exhibited performance variance.
  • Increasing neural network depth improved performance up to a saturation point.

Conclusions:

  • The ELM framework facilitates effective machine learning deployment on edge devices, matching desktop performance.
  • ANNs are generally the most effective, but algorithm choice depends on specific application constraints and datasets.
  • The open-source ELM framework supports the developer community in selecting and comparing algorithms for embedded systems.