Enhancing Endurance of Non Volatile Memory in Embedded Systems: Based on Optimized Machine Learning and Compression Techniques

Price: $74.00
(as of Nov 27,2024 20:14:42 UTC – Details)

Publisher ‏ : ‎ LAP LAMBERT Academic Publishing (June 14, 2024)
Language ‏ : ‎ English
Paperback ‏ : ‎ 136 pages
ISBN-10 ‏ : ‎ 620780483X
ISBN-13 ‏ : ‎ 978-6207804832
Item Weight ‏ : ‎ 7.4 ounces
Dimensions ‏ : ‎ 5.91 x 0.31 x 8.66 inches

In today’s fast-paced technological landscape, embedded systems play a crucial role in various industries such as automotive, healthcare, and consumer electronics. These systems rely on non-volatile memory (NVM) to store and retrieve data efficiently. However, the endurance of NVM can be a limiting factor in the overall performance and reliability of embedded systems.

To address this challenge, researchers and engineers have been exploring ways to enhance the endurance of NVM through optimized machine learning and compression techniques. By leveraging the power of machine learning algorithms, data patterns and access frequencies can be analyzed to optimize the storage and retrieval processes, reducing the wear and tear on NVM cells.

Additionally, data compression techniques can be implemented to reduce the amount of data written to NVM, further extending its lifespan. By compressing data before it is stored and decompressing it when it is retrieved, the number of write operations can be minimized, ultimately improving the endurance of NVM.

In this post, we will delve into the latest advancements in enhancing the endurance of NVM in embedded systems through the integration of optimized machine learning and compression techniques. Stay tuned for insights on how these innovative approaches are revolutionizing the field of embedded systems and paving the way for more resilient and efficient technologies.
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