Integrated Image Sensor and Light Convolutional Neural Network for Image Classification

Cheng Jian Lin, Chun Hui Lin, Shyh Hau Wang

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


Deep learning has accomplished huge success in computer vision applications such as self-driving vehicles, facial recognition, and controlling robots. A growing need for deploying systems on resource-limited or resource-constrained environments such as smart cameras, autonomous vehicles, robots, smartphones, and smart wearable devices drives one of the current mainstream developments of convolutional neural networks: reducing model complexity but maintaining fine accuracy. In this study, the proposed efficient light convolutional neural network (ELNet) comprises three convolutional modules which perform ELNet using fewer computations, which is able to be implemented in resource-constrained hardware equipment. The classification task using CIFAR-10 and CIFAR-100 datasets was used to verify the model performance. According to the experimental results, ELNet reached 92.3% and 69%, respectively, in CIFAR-10 and CIFAR-100 datasets; moreover, ELNet effectively lowered the computational complexity and parameters required in comparison with other CNN architectures.

Original languageEnglish
Article number5573031
JournalMathematical Problems in Engineering
Publication statusPublished - 2021

All Science Journal Classification (ASJC) codes

  • Mathematics(all)
  • Engineering(all)


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