LSHIM: Low-Power and Small-Area Inexact Multiplier for High-Speed Error-Resilient Applications

Numerical computations in various applications can often tolerate a small degree of error. In fields such as data mining, encoding algorithms, image processing, machine learning, and signal processing where error resilience is crucial approximate computing can effectively replace precise computing t...

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Veröffentlicht in:	IEEE journal on emerging and selected topics in circuits and systems Jg. 15; H. 1; S. 94 - 104
Hauptverfasser:	Izadi, Azin, Jamshidi, Vahid
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.03.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accuracy Adders Algorithms approximate computing Approximate multiplier Approximation algorithms area Arithmetic Circuits Computation Data mining Delay Delays Energy consumption Image processing Machine learning Mitchell’s multiplication algorithm Multiplication Multipliers Performance evaluation Power consumption Power demand Resilience Signal processing algorithms
ISSN:	2156-3357, 2156-3365
Online-Zugang:	Volltext
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