Enhancing DNN Computational Efficiency via Decomposition and Approximation

Enhancing DNN Computational Efficiency via Decomposition and Approximation

The increasing computational demands of emerging deep neural networks (DNNs) are fueled by their extensive computation intensity across various tasks, placing a significant strain on resources. This paper introduces DART, an adaptive microarchitecture that enhances area, power, and energy efficiency...

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Bibliographic Details
Main Authors: Ori Schweitzer, Uri Weiser, Freddy Gabbay
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Approximate computing
computer architectures
deep neural networks
machine learning accelerators
Online Access:https://ieeexplore.ieee.org/document/10813351/
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https://ieeexplore.ieee.org/document/10813351/

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