Energy efficient design and implementation of approximate adder for image processing applications
Approximate computing is a new technique that promises to speed up computations while preserving a level of precision suitable for error-tolerant systems such as neural networks and graphics. At the edge, a lot of computationally complex methods are now in use. As such, designing quick a...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Faculty of Technical Sciences in Cacak
2025-01-01
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| Series: | Serbian Journal of Electrical Engineering |
| Subjects: | |
| Online Access: | https://doiserbia.nb.rs/img/doi/1451-4869/2025/1451-48692501075N.pdf |
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| Summary: | Approximate computing is a new technique that promises to speed up
computations while preserving a level of precision suitable for
error-tolerant systems such as neural networks and graphics. At the edge, a
lot of computationally complex methods are now in use. As such, designing
quick and low-energy circuits is crucial. This work presents a novel
approximate full adder approach that lowers power consumption and delay at
the expense of some output mistakes. To achieve these objectives, the
proposed full adder architecture makes use of fundamental gate logic
reduction techniques. Evaluations based on the Intel FPGA synthesis tool
indicate that the suggested adder surpasses state-of-the-art techniques in
terms of power, speed, and propagation delay. The design parameters - area,
power dissipation, and latent characteristics of proposed adder are verified
by simulation using EDA tools. The results demonstrate that our proposed
approximate adder runs faster and requires fewer logic components than
earlier equivalent systems. The synthesis reports testify to the fact that
compared to other adders currently in use, the suggested adder used less
logic elements. Furthermore, suggested approximation adders were used to
execute image additions. Using image addition, the image quantitative
statistics are used to application-level accuracy metrics analysis.
Quantitative results confirm the superior functioning of the full adder cell
approximation over comparable models. |
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| ISSN: | 1451-4869 2217-7183 |