TiQSA: Workload Minimization in Convolutional Neural Networks Using Tile Quantization and Symmetry Approximation

Dilshad Sabir, Muhammmad Abdullah Hanif, Ali Hassan, Saad Rehman, Muhammad Shafique

Research output: Contribution to journalArticlepeer-review

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 languageEnglish (US)
Article number9389774
Pages (from-to)53647-53668
Number of pages22
JournalIEEE Access
Volume9
DOIs
StatePublished - 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

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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