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 in convolution 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 <inline-formula> <tex-math notation="LaTeX">\sim 5.28\text{x} </tex-math></inline-formula> multiplicative workload reduction without significant accuracy degradation (<0.1%) for ImageNet on ResNet-18, which is <inline-formula> <tex-math notation="LaTeX">\sim 1.08\text{x} </tex-math></inline-formula> less multiplicative workload as compared to state-of-the-art Winograd CNN pruning. For LeNet, <inline-formula> <tex-math notation="LaTeX">\sim 3.87\text{x} </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">\sim 3.93\text{x} </tex-math></inline-formula> was the multiplicative workload reduction for MNIST and Fashion-MNIST datasets. The additive workload reduction was <inline-formula> <tex-math notation="LaTeX">\sim 2.5\text{x} </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">\sim 2.56\text{x} </tex-math></inline-formula> for the respective datasets. There is no significant accuracy loss for MNIST and Fashion-MNIST dataset.