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An Overview of Machine Learning within Embedded and Mobile Devices-Optimizations and Applications.

Taiwo Samuel Ajani1, Agbotiname Lucky Imoize1,2, Aderemi A Atayero3

  • 1Department of Electrical and Electronics Engineering, Faculty of Engineering, University of Lagos, Akoka 100213, Lagos State, Nigeria.

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Summary
This summary is machine-generated.

Embedded machine learning (EML) faces challenges due to algorithm intensity. This survey explores optimization techniques for efficient EML implementation in resource-constrained devices.

Keywords:
TinyMLcomputer architecturedeep learningembedded computing systemsmachine learningmobile computingmobile devicesoptimization techniques

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Artificial Intelligence

Background:

  • Embedded systems are evolving with advances in computer architecture and machine learning.
  • Embedded machine learning (EML) has applications in computer vision, speech recognition, healthcare, and robotics.
  • Resource constraints in embedded and mobile devices pose challenges for computationally intensive ML algorithms.

Purpose of the Study:

  • To survey current research trends in embedded machine learning.
  • To explore optimization techniques for implementing ML algorithms in resource-limited environments.
  • To provide an overview of EML applications and future research directions.

Main Methods:

  • Overview of compute-intensive machine learning algorithms (HMM, k-NN, SVM, GMM, DNN).
  • Analysis of optimization techniques for resource-constrained environments.
  • Discussion of implementation on microcontrollers, mobile devices, and hardware accelerators.

Main Results:

  • Identification of key machine learning algorithms and their computational demands.
  • Exploration of various optimization strategies for efficient EML.
  • Examination of EML implementation across different hardware platforms.

Conclusions:

  • EML requires innovative optimization techniques at both algorithmic and hardware levels.
  • Key application areas and future research directions in EML are highlighted.
  • Lessons learned provide insights for future exploration in the embedded machine learning domain.