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

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

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 h...

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Bibliographic Details
Main Authors: Dilshad Sabir, Muhammmad Abdullah Hanif, Ali Hassan, Saad Rehman, Muhammad Shafique
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Convolutional neural network
reduced workload
winograd transform
particle of swarm convolution layer optimization
symmetry approximation
tile quantization approximation
Online Access:https://ieeexplore.ieee.org/document/9389774/

Internet

https://ieeexplore.ieee.org/document/9389774/

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