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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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10813351/ |
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