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...
Main Authors: | Dilshad Sabir, Muhammmad Abdullah Hanif, Ali Hassan, Saad Rehman, Muhammad Shafique |
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Format: | Article |
Language: | English |
Published: |
IEEE
2021-01-01
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Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9389774/ |
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