CAxCNN: Towards the Use of Canonic Sign Digit Based Approximation for Hardware-Friendly Convolutional Neural Networks

CAxCNN: Towards the Use of Canonic Sign Digit Based Approximation for Hardware-Friendly Convolutional Neural Networks

The design of hardware-friendly architectures with low computational overhead is desirable for low latency realization of CNN on resource-constrained embedded platforms. In this work, we propose CAxCNN, a Canonic Sign Digit (CSD) based approximation methodology for representing the filter weights of...

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Bibliographic Details
Main Authors: Mohsin Riaz, Rehan Hafiz, Salman Abdul Khaliq, Muhammad Faisal, Hafiz Talha Iqbal, Mohsen Ali, Muhammad Shafique
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Convolution neural networks
dedicated accelerators
approximate computing
canonic sign digits
Online Access:https://ieeexplore.ieee.org/document/9137167/

Internet

https://ieeexplore.ieee.org/document/9137167/

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