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Verfasst von:Seiler, Fabian [VerfasserIn]   i
 Taherinejad, Nima [VerfasserIn]   i
Titel:Efficient image processing via memristive-based approximate in-memory computing
Verf.angabe:Fabian Seiler and Nima TaheriNejad, member, IEEE
Jahr:2024
Umfang:12 S.
Illustrationen:Illustrationen, Diagramme
Fussnoten:Gesehen am 21.05.2025
Titel Quelle:Enthalten in: Institute of Electrical and Electronics EngineersIEEE transactions on computer-aided design of integrated circuits and systems
Ort Quelle:New York, NY : Institute of Electrical and Electronics Engineers, 1982
Jahr Quelle:2024
Band/Heft Quelle:43(2024), 11, Seite 3312-3323
ISSN Quelle:1937-4151
Abstract:Image processing algorithms continue to demand higher performance from computers. However, computer performance is not improving at the same rate as before. In response to the current challenges in enhancing computing performance, a wave of new technologies and computing paradigms is surfacing. Among these, memristors stand out as one of the most promising components due to their technological prospects and low power consumption. With efficient data storage capabilities and their ability to directly perform logical operations within the memory, they are well-suited for in-memory computation (IMC). Approximate computing emerges as another promising paradigm, offering improved performance metrics, notably speed. The tradeoff for this gain is the reduction of accuracy. In this article, we are using the stateful logic material implication (IMPLY) in the semi-serial topology and combine both the paradigms to further enhance the computational performance. We present three novel approximated adders that drastically improve speed and energy consumption with an normalized mean error distance (NMED) lower than 0.02 for most scenarios. We evaluated partially approximated Ripple carry adder (RCA) at the circuit-level and compared them to the State-of-the-Art (SoA). The proposed adders are applied in different image processing applications and the quality metrics are calculated. While maintaining acceptable quality, our approach achieves significant energy savings of 6%-38% and reduces the delay (number of computation cycles) by 5%-35%, demonstrating notable efficiency compared to exact calculations.
DOI:doi:10.1109/TCAD.2024.3438113
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

kostenfrei: Volltext: https://doi.org/10.1109/TCAD.2024.3438113
 kostenfrei: Volltext: https://ieeexplore.ieee.org/document/10745792
 DOI: https://doi.org/10.1109/TCAD.2024.3438113
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:Adders
 Approximate
 Approximate computing
 Computational efficiency
 Energy consumption
 image processing
 Image processing
 IMPLY
 in-memory computing
 In-memory computing
 Logic
 memristor
 Memristors
 Power demand
 Topology
K10plus-PPN:1926221559
Verknüpfungen:→ Zeitschrift

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