Enhancing Endurance of Non Volatile Memory in Embedded Systems: Based on Optimized Machine Learning and Compression Techniques - Brossura
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This book is focused on enhancing the endurance of Non-Volatile Random Access Memory (NVRAM) for embedded systems applications. It describes the methodology that combines optimized machine learning algorithms based on workload prediction and data compression techniques to prolong the lifespan of NVRAM. The framework utilizes an Instruction Per Cycle-based Dynamic Pattern Compression model to analyze and compress workloads, as well as a Workload Hybrid Energy Adaptive Learning model to categorize and further compress data for storage. The book provides a solution for improving NVRAM endurance, which is crucial for the performance of embedded devices, by addressing workload prediction and efficient compression.
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Enhancing Endurance of Non Volatile Memory in Embedded Systems
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PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Codice articolo L0-9786207804832
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Enhancing Endurance of Non Volatile Memory in Embedded Systems: Based on Optimized Machine Learning and Compression Techniques
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LAP LAMBERT Academic Publishing, 2024
ISBN 10: 620780483X
ISBN 13: 9786207804832
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Enhancing Endurance of Non Volatile Memory in Embedded Systems
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Enhancing Endurance of Non Volatile Memory in Embedded Systems
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LAP LAMBERT Academic Publishing Jun 2024, 2024
ISBN 10: 620780483X
ISBN 13: 9786207804832
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 136 pp. Englisch. Codice articolo 9786207804832
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Enhancing Endurance of Non Volatile Memory in Embedded Systems | Based on Optimized Machine Learning and Compression Techniques
Editore:
LAP LAMBERT Academic Publishing, 2024
ISBN 10: 620780483X
ISBN 13: 9786207804832
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Taschenbuch. Condizione: Neu. Enhancing Endurance of Non Volatile Memory in Embedded Systems | Based on Optimized Machine Learning and Compression Techniques | Shritharanyaa J P (u. a.) | Taschenbuch | Englisch | 2024 | LAP LAMBERT Academic Publishing | EAN 9786207804832 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu. Codice articolo 129561931
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Enhancing Endurance of Non Volatile Memory in Embedded Systems
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LAP LAMBERT Academic Publishing Jun 2024, 2024
ISBN 10: 620780483X
ISBN 13: 9786207804832
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book is focused on enhancing the endurance of Non-Volatile Random Access Memory (NVRAM) for embedded systems applications. It describes the methodology that combines optimized machine learning algorithms based on workload prediction and data compression techniques to prolong the lifespan of NVRAM. The framework utilizes an Instruction Per Cycle-based Dynamic Pattern Compression model to analyze and compress workloads, as well as a Workload Hybrid Energy Adaptive Learning model to categorize and further compress data for storage. The book provides a solution for improving NVRAM endurance, which is crucial for the performance of embedded devices, by addressing workload prediction and efficient compression.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 136 pp. Englisch. Codice articolo 9786207804832
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Enhancing Endurance of Non Volatile Memory in Embedded Systems : Based on Optimized Machine Learning and Compression Techniques
Editore:
LAP LAMBERT Academic Publishing, 2024
ISBN 10: 620780483X
ISBN 13: 9786207804832
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Da: AHA-BUCH GmbH, Einbeck, Germania
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book is focused on enhancing the endurance of Non-Volatile Random Access Memory (NVRAM) for embedded systems applications. It describes the methodology that combines optimized machine learning algorithms based on workload prediction and data compression techniques to prolong the lifespan of NVRAM. The framework utilizes an Instruction Per Cycle-based Dynamic Pattern Compression model to analyze and compress workloads, as well as a Workload Hybrid Energy Adaptive Learning model to categorize and further compress data for storage. The book provides a solution for improving NVRAM endurance, which is crucial for the performance of embedded devices, by addressing workload prediction and efficient compression. Codice articolo 9786207804832
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