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Titlebook: Approximate Circuits; Methodologies and CA Sherief Reda,Muhammad Shafique Book 2019 Springer Nature Switzerland AG 2019 approximate by desi

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11#
發(fā)表于 2025-3-23 10:22:42 | 只看該作者
12#
發(fā)表于 2025-3-23 16:21:10 | 只看該作者
Approximate Hardware Generation Using Formal TechniquesFor this reason, we further present a heuristic approach, which uses Symbolic Computer Algebra to determine the error-metric. This approach is tailored for arithmetic circuits. We apply this method to Ripple-Carry-Adders and compare the results to state-of-the-art handcrafted approximate hardware.
13#
發(fā)表于 2025-3-23 20:14:55 | 只看該作者
14#
發(fā)表于 2025-3-23 22:16:49 | 只看該作者
s.Discusses effective memory approximation techniques to emp.This book provides readers with a comprehensive, state-of-the-art overview of approximate computing, enabling the design trade-off of accuracy for achieving better power/performance efficiencies, through the simplification of underlying co
15#
發(fā)表于 2025-3-24 05:13:00 | 只看該作者
16#
發(fā)表于 2025-3-24 07:42:59 | 只看該作者
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發(fā)表于 2025-3-24 11:27:29 | 只看該作者
Mine: Unechte Mitarbeiter-Partizipation,straints with the goal of obtaining significant gains in computational throughput while maintaining an acceptable quality of results. In this chapter, we review the wide spectrum of approximate computing techniques that have been successfully applied to DNNs.
18#
發(fā)表于 2025-3-24 18:06:27 | 只看該作者
19#
發(fā)表于 2025-3-24 20:18:43 | 只看該作者
Automated Search-Based Functional Approximation for Digital Circuitsd accelerating the search process. Case studies dealing with approximate implementations of arithmetic circuits and image operators are presented to highlight the quality of results obtained by the search-based functional approximation in completely different application domains.
20#
發(fā)表于 2025-3-25 02:28:40 | 只看該作者
Approximate Computing Techniques for Deep Neural Networksstraints with the goal of obtaining significant gains in computational throughput while maintaining an acceptable quality of results. In this chapter, we review the wide spectrum of approximate computing techniques that have been successfully applied to DNNs.
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