Error Correction Codes for Non-Volatile Memories - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Error Correction Codes for Non-Volatile Memories write by Rino Micheloni. This book was released on 2008-06-03. Error Correction Codes for Non-Volatile Memories available in PDF, EPUB and Kindle. Nowadays it is hard to find an electronic device which does not use codes: for example, we listen to music via heavily encoded audio CD's and we watch movies via encoded DVD's. There is at least one area where the use of encoding/decoding is not so developed, yet: Flash non-volatile memories. Flash memory high-density, low power, cost effectiveness, and scalable design make it an ideal choice to fuel the explosion of multimedia products, like USB keys, MP3 players, digital cameras and solid-state disk. In ECC for Non-Volatile Memories the authors expose the basics of coding theory needed to understand the application to memories, as well as the relevant design topics, with reference to both NOR and NAND Flash architectures. A collection of software routines is also included for better understanding. The authors form a research group (now at Qimonda) which is the typical example of a fruitful collaboration between mathematicians and engineers.
VLSI-Design of Non-Volatile Memories
VLSI-Design of Non-Volatile Memories - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook VLSI-Design of Non-Volatile Memories write by Giovanni Campardo. This book was released on 2005-01-18. VLSI-Design of Non-Volatile Memories available in PDF, EPUB and Kindle. VLSI-Design for Non-Volatile Memories is intended for electrical engineers and graduate students who want to enter into the integrated circuit design world. Non-volatile memories are treated as an example to explain general design concepts. Practical illustrative examples of non-volatile memories, including flash types, are showcased to give insightful examples of the discussed design approaches. A collection of photos is included to make the reader familiar with silicon aspects. Throughout all parts of this book, the authors have taken a practical and applications-driven point of view, providing a comprehensive and easily understood approach to all the concepts discussed. Giovanni Campardo and Rino Micheloni have a solid track record of leading design activities at the STMicroelectronics Flash Division. David Novosel is President and founder of Intelligent Micro Design, Inc., Pittsburg, PA.
Coding Techniques for Error Correction and Rewriting in Flash Memories
Coding Techniques for Error Correction and Rewriting in Flash Memories - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Coding Techniques for Error Correction and Rewriting in Flash Memories write by Shoeb Ahmed Mohammed. This book was released on 2010. Coding Techniques for Error Correction and Rewriting in Flash Memories available in PDF, EPUB and Kindle. Flash memories have become the main type of non-volatile memories. They are widely used in mobile, embedded and mass-storage devices. Flash memories store data in floating-gate cells, where the amount of charge stored in cells 0́3 called cell levels 0́3 is used to represent data. To reduce the level of any cell, a whole cell block (about 106 cells) must be erased together and then reprogrammed. This operation, called block erasure, is very costly and brings significant challenges to cell programming and rewriting of data. To address these challenges, rank modulation and rewriting codes have been proposed for reliably storing and modifying data. However, for these new schemes, many problems still remain open. In this work, we study error-correcting rank-modulation codes and rewriting codes for flash memories. For the rank modulation scheme, we study a family of one- error-correcting codes, and present efficient encoding and decoding algorithms. For rewriting, we study a family of linear write-once memory (WOM) codes, and present an effective algorithm for rewriting using the codes. We analyze the performance of our solutions for both schemes.
Channel and Source Coding for Non-Volatile Flash Memories
Channel and Source Coding for Non-Volatile Flash Memories - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Channel and Source Coding for Non-Volatile Flash Memories write by Mohammed Rajab. This book was released on 2020-01-02. Channel and Source Coding for Non-Volatile Flash Memories available in PDF, EPUB and Kindle. Mohammed Rajab proposes different technologies like the error correction coding (ECC), sources coding and offset calibration that aim to improve the reliability of the NAND flash memory with low implementation costs for industrial application. The author examines different ECC schemes based on concatenated codes like generalized concatenated codes (GCC) which are applicable for NAND flash memories by using the hard and soft input decoding. Furthermore, different data compression schemes are examined in order to reduce the write amplification effect and also to improve the error correct capability of the ECC by combining both schemes.
Machine Learning and Non-volatile Memories
Machine Learning and Non-volatile Memories - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Machine Learning and Non-volatile Memories write by Rino Micheloni. This book was released on 2022-05-25. Machine Learning and Non-volatile Memories available in PDF, EPUB and Kindle. This book presents the basics of both NAND flash storage and machine learning, detailing the storage problems the latter can help to solve. At a first sight, machine learning and non-volatile memories seem very far away from each other. Machine learning implies mathematics, algorithms and a lot of computation; non-volatile memories are solid-state devices used to store information, having the amazing capability of retaining the information even without power supply. This book will help the reader understand how these two worlds can work together, bringing a lot of value to each other. In particular, the book covers two main fields of application: analog neural networks (NNs) and solid-state drives (SSDs). After reviewing the basics of machine learning in Chapter 1, Chapter 2 shows how neural networks can mimic the human brain; to accomplish this result, neural networks have to perform a specific computation called vector-by-matrix (VbM) multiplication, which is particularly power hungry. In the digital domain, VbM is implemented by means of logic gates which dictate both the area occupation and the power consumption; the combination of the two poses serious challenges to the hardware scalability, thus limiting the size of the neural network itself, especially in terms of the number of processable inputs and outputs. Non-volatile memories (phase change memories in Chapter 3, resistive memories in Chapter 4, and 3D flash memories in Chapter 5 and Chapter 6) enable the analog implementation of the VbM (also called “neuromorphic architecture”), which can easily beat the equivalent digital implementation in terms of both speed and energy consumption. SSDs and flash memories are strictly coupled together; as 3D flash scales, there is a significant amount of work that has to be done in order to optimize the overall performances of SSDs. Machine learning has emerged as a viable solution in many stages of this process. After introducing the main flash reliability issues, Chapter 7 shows both supervised and un-supervised machine learning techniques that can be applied to NAND. In addition, Chapter 7 deals with algorithms and techniques for a pro-active reliability management of SSDs. Last but not least, the last section of Chapter 7 discusses the next challenge for machine learning in the context of the so-called computational storage. No doubt that machine learning and non-volatile memories can help each other, but we are just at the beginning of the journey; this book helps researchers understand the basics of each field by providing real application examples, hopefully, providing a good starting point for the next level of development.
