Energy-efficient Image Classification on Edge Devices: Implementation and Evaluation

Year : 2024 | Volume : 11 | Issue : 03 | Page : 10 18
    By

    Shital Thakkar,

  • Arpit Acharya,

  • Richa Singh,

  • Harsh Bhalala,

  • Pinkesh Patel,

  • Vinay Thumar,

  1. Associate Professor, Department of Electronics and Communication Engineering, Dharmsinh Desai University (DDU), Nadiad, Gujarat, India
  2. Student, Department of Electronics and Communication Engineering, Dharmsinh Desai University (DDU), Nadiad, Gujarat, India
  3. Student, Department of Electronics and Communication Engineering, Dharmsinh Desai University (DDU), Nadiad, Gujarat, India
  4. Student, Department of Electronics and Communication Engineering, Dharmsinh Desai University (DDU), Nadiad, Gujarat, India
  5. Student, Department of Electronics and Communication Engineering, Dharmsinh Desai University (DDU), Nadiad, Gujarat, India
  6. Student, Department of Electronics and Communication Engineering, Dharmsinh Desai University (DDU), Nadiad, Gujarat, India

Abstract

Image classification is a computer vision problem where an algorithm determines a class or label for a given image. Various real-time applications like object recognition, medical diagnosis, person recognition, etc. Image classification property on edge devices is useful for autonomous vehicles, surveillance, and healthcare and internet of things deployments. The advancement of deep learning based methods and graphics processing units (GPU) devices allows efficient processing locally. The study utilizes a GPU-powered board, recognized for its strength in visual computing, to run a convolutional neural network (CNN) classification algorithm. Deep learning with GPUs speeds up image analysis by extracting features and constructing neural networks, enabling efficient processing of both static and real-time data. The combination of edge device efficiency and GPU acceleration illustrates the potential for strong image classification in practical scenarios. We demonstrated classification tasks such as binary classification of cats versus dogs and multi-class classification on the CIFAR10 benchmark dataset.

Keywords: GPU, deep learning, convolution neural networks (CNN), CIFAR10, computer vision

[This article belongs to Journal of Image Processing & Pattern Recognition Progress ]

How to cite this article:
Shital Thakkar, Arpit Acharya, Richa Singh, Harsh Bhalala, Pinkesh Patel, Vinay Thumar. Energy-efficient Image Classification on Edge Devices: Implementation and Evaluation. Journal of Image Processing & Pattern Recognition Progress. 2024; 11(03):10-18.
How to cite this URL:
Shital Thakkar, Arpit Acharya, Richa Singh, Harsh Bhalala, Pinkesh Patel, Vinay Thumar. Energy-efficient Image Classification on Edge Devices: Implementation and Evaluation. Journal of Image Processing & Pattern Recognition Progress. 2024; 11(03):10-18. Available from: https://journals.stmjournals.com/joipprp/article=2024/view=177853


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Regular Issue Subscription Review Article
Volume 11
Issue 03
Received 14/08/2024
Accepted 26/09/2024
Published 11/10/2024
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