## Abstract

Convolutional Neural Networks (CNNs) in the Internet-of-Things (IoT)-based applications face stringent constraints, like limited memory capacity and energy resources due to many computations inconvolution layers. In order to reduce the computational workload in these layers, this paper proposes a hybrid convolution method in conjunction with a Particle of Swarm Convolution Layer Optimization (PSCLO) algorithm. The hybrid convolution is an approximation that exploits the inherent symmetry of filter termed as symmetry approximation and Winograd algorithm structure termed as tile quantization approximation. PSCLO optimizes the balance between workload reduction and accuracy degradation for each convolution layer by selecting fine-tuned thresholds to control each approximation's intensity. The proposed methods have been evaluated on ImageNet, MNIST, Fashion-MNIST, SVHN, and CIFAR-10 datasets. The proposed techniques achieved 5.28\text{x} multiplicative workload reduction without significant accuracy degradation (<0.1%) for ImageNet on ResNet-18, which is 1.08\text{x} less multiplicative workload as compared to state-of-the-art Winograd CNN pruning. For LeNet 3.87\text{x} and 3.93\text{x} was the multiplicative workload reduction for MNIST and Fashion-MNISTdatasets. The additive workload reduction was 2.5 {x} and 2.56 {x} for the respective datasets. There is no significant accuracy loss for MNIST and Fashion-MNIST dataset.

Original language | English (US) |
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Article number | 9389774 |

Pages (from-to) | 53647-53668 |

Number of pages | 22 |

Journal | IEEE Access |

Volume | 9 |

DOIs | |

State | Published - 2021 |

## Keywords

- CNN
- Convolutional neural network
- DNN
- particle of swarm convolution layer optimization
- reduced workload
- symmetry approximation
- tile quantization approximation
- winograd transform

## ASJC Scopus subject areas

- General Computer Science
- General Materials Science
- General Engineering