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Author: Xiao Liu
Publisher:
ISBN:
Category :
Languages : en
Pages : 163
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Book Description
The emerging Non-Volatile Memory (NVM) technologies are reforming the computer architecture. NVM holds advantages includes a byte-addressable interface, low latency, high capacity, and in-memory computing capability. However, data-intensive applications today demand compound features rather than just better performance. For instance, big data applications would require high availability and reliability. The neural network applications require scalability and power efficiency. Despite all the advantages of NVM, simply attaching the NVM to the memory hierarchy are unable to meet these demands. The decoupled reliability schemes among NVM and other devices fail to provide sufficient reliability. The vulnerability against overheating and hardware underutilization limit the performance and scalability of the in-memory computing NVM.Using the NVM for the data-intensive application requires redesign and customization. In this thesis, we focus on discussing the architecture designs that enable NVM for data-intensive applications. Our study includes two major types of data-intensive applications--big data applications and neural network applications. We first conduct a characteristic study against the persistent memory applications. Persistent memory implements over the NVM-based main memory and guarantees crash consistency. We explore the performance interaction across applications, persistent memory system software, and hardware components. Based on our characterization results, we provide a set of implications and recommendations for optimizing persistent memory designs. Second, we propose Binary Star for the generic data-intensive applications, which coordinates the reliability schemes and consistent cache writeback between 3D-stacked DRAM last-level cache and NVM main memory to maintain the reliability of the memory hierarchy. Binary Star significantly reduces the performance and storage overhead of consistent cache writeback by coordinating it with NVM wear leveling. For neural network applications, our first design explores the thermal effect over one representative NVM--resistive memory (RRAM). We find heat-induced interference decreases the computational accuracy in the RRAM-based neural network accelerator. We propose HR3AM, a heat resilience design, which improves accuracy and optimizes the thermal distribution. Results show that HR3AM improves classification accuracy and decreases both the maximum and average chip temperatures. Lastly, we present Mirage to improve parallelism and flexibility for pipeline-enabled RRAM-based accelerators. Mirage is a hardware/software co-design that addresses the data dependencies and inflexibility issues of existing accelerators. Our evaluation shows that Mirage achieves low inference latency and high throughput compared to state-of-the-art RRAM-based accelerators.
Author: Xiao Liu
Publisher:
ISBN:
Category :
Languages : en
Pages : 163
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Book Description
The emerging Non-Volatile Memory (NVM) technologies are reforming the computer architecture. NVM holds advantages includes a byte-addressable interface, low latency, high capacity, and in-memory computing capability. However, data-intensive applications today demand compound features rather than just better performance. For instance, big data applications would require high availability and reliability. The neural network applications require scalability and power efficiency. Despite all the advantages of NVM, simply attaching the NVM to the memory hierarchy are unable to meet these demands. The decoupled reliability schemes among NVM and other devices fail to provide sufficient reliability. The vulnerability against overheating and hardware underutilization limit the performance and scalability of the in-memory computing NVM.Using the NVM for the data-intensive application requires redesign and customization. In this thesis, we focus on discussing the architecture designs that enable NVM for data-intensive applications. Our study includes two major types of data-intensive applications--big data applications and neural network applications. We first conduct a characteristic study against the persistent memory applications. Persistent memory implements over the NVM-based main memory and guarantees crash consistency. We explore the performance interaction across applications, persistent memory system software, and hardware components. Based on our characterization results, we provide a set of implications and recommendations for optimizing persistent memory designs. Second, we propose Binary Star for the generic data-intensive applications, which coordinates the reliability schemes and consistent cache writeback between 3D-stacked DRAM last-level cache and NVM main memory to maintain the reliability of the memory hierarchy. Binary Star significantly reduces the performance and storage overhead of consistent cache writeback by coordinating it with NVM wear leveling. For neural network applications, our first design explores the thermal effect over one representative NVM--resistive memory (RRAM). We find heat-induced interference decreases the computational accuracy in the RRAM-based neural network accelerator. We propose HR3AM, a heat resilience design, which improves accuracy and optimizes the thermal distribution. Results show that HR3AM improves classification accuracy and decreases both the maximum and average chip temperatures. Lastly, we present Mirage to improve parallelism and flexibility for pipeline-enabled RRAM-based accelerators. Mirage is a hardware/software co-design that addresses the data dependencies and inflexibility issues of existing accelerators. Our evaluation shows that Mirage achieves low inference latency and high throughput compared to state-of-the-art RRAM-based accelerators.
Author: Joy Arulraj
Publisher: Springer Nature
ISBN: 3031018680
Category : Computers
Languages : en
Pages : 173
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Book Description
This book explores the implications of non-volatile memory (NVM) for database management systems (DBMSs). The advent of NVM will fundamentally change the dichotomy between volatile memory and durable storage in DBMSs. These new NVM devices are almost as fast as volatile memory, but all writes to them are persistent even after power loss. Existing DBMSs are unable to take full advantage of this technology because their internal architectures are predicated on the assumption that memory is volatile. With NVM, many of the components of legacy DBMSs are unnecessary and will degrade the performance of data-intensive applications. We present the design and implementation of DBMS architectures that are explicitly tailored for NVM. The book focuses on three aspects of a DBMS: (1) logging and recovery, (2) storage and buffer management, and (3) indexing. First, we present a logging and recovery protocol that enables the DBMS to support near-instantaneous recovery. Second, we propose a storage engine architecture and buffer management policy that leverages the durability and byte-addressability properties of NVM to reduce data duplication and data migration. Third, the book presents the design of a range index tailored for NVM that is latch-free yet simple to implement. All together, the work described in this book illustrates that rethinking the fundamental algorithms and data structures employed in a DBMS for NVM improves performance and availability, reduces operational cost, and simplifies software development.
Author: Yuan Xie
Publisher: Springer Science & Business Media
ISBN: 144199551X
Category : Technology & Engineering
Languages : en
Pages : 321
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Book Description
This book explores the design implications of emerging, non-volatile memory (NVM) technologies on future computer memory hierarchy architecture designs. Since NVM technologies combine the speed of SRAM, the density of DRAM, and the non-volatility of Flash memory, they are very attractive as the basis for future universal memories. This book provides a holistic perspective on the topic, covering modeling, design, architecture and applications. The practical information included in this book will enable designers to exploit emerging memory technologies to improve significantly the performance/power/reliability of future, mainstream integrated circuits.
Author: Suting Han
Publisher: Woodhead Publishing
ISBN: 0128226064
Category : Technology & Engineering
Languages : en
Pages : 352
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Book Description
Photo-Electroactive Non-Volatile Memories for Data Storage and Neuromorphic Computing summarizes advances in the development of photo-electroactive memories and neuromorphic computing systems, suggests possible solutions to the challenges of device design, and evaluates the prospects for commercial applications. Sections covers developments in electro-photoactive memory, and photonic neuromorphic and in-memory computing, including discussions on design concepts, operation principles and basic storage mechanism of optoelectronic memory devices, potential materials from organic molecules, semiconductor quantum dots to two-dimensional materials with desirable electrical and optical properties, device challenges, and possible strategies. This comprehensive, accessible and up-to-date book will be of particular interest to graduate students and researchers in solid-state electronics. It is an invaluable systematic introduction to the memory characteristics, operation principles and storage mechanisms of the latest reported electro-photoactive memory devices. Reviews the most promising materials to enable emerging computing memory and data storage devices, including one- and two-dimensional materials, metal oxides, semiconductors, organic materials, and more Discusses fundamental mechanisms and design strategies for two- and three-terminal device structures Addresses device challenges and strategies to enable translation of optical and optoelectronic technologies
Author: Cybellium Ltd
Publisher: Cybellium Ltd
ISBN:
Category : Computers
Languages : en
Pages : 171
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Book Description
Unlock the Power of Effective Data Storage and Processing with "Mastering Data Storage and Processing" In today's data-driven world, the ability to store, manage, and process data effectively is the cornerstone of success. "Mastering Data Storage and Processing" is your definitive guide to mastering the art of seamlessly managing and processing data for optimal performance and insights. Whether you're an experienced data professional or a newcomer to the realm of data management, this book equips you with the knowledge and skills needed to navigate the intricacies of modern data storage and processing. About the Book: "Mastering Data Storage and Processing" takes you on an enlightening journey through the intricacies of data storage and processing, from foundational concepts to advanced techniques. From storage systems to data pipelines, this book covers it all. Each chapter is meticulously designed to provide both a deep understanding of the concepts and practical applications in real-world scenarios. Key Features: · Foundational Principles: Build a strong foundation by understanding the core principles of data storage technologies, file systems, and data processing paradigms. · Storage Systems: Explore a range of data storage systems, from relational databases and NoSQL databases to cloud-based storage solutions, understanding their strengths and applications. · Data Modeling and Design: Learn how to design effective data schemas, optimize storage structures, and establish relationships for efficient data organization. · Data Processing Paradigms: Dive into various data processing paradigms, including batch processing, stream processing, and real-time analytics, for extracting valuable insights. · Big Data Technologies: Master the essentials of big data technologies such as Hadoop, Spark, and distributed computing frameworks for processing massive datasets. · Data Pipelines: Understand the design and implementation of data pipelines for data ingestion, transformation, and loading, ensuring seamless data flow. · Scalability and Performance: Discover strategies for optimizing data storage and processing systems for scalability, fault tolerance, and high performance. · Real-World Use Cases: Gain insights from real-world examples across industries, from finance and healthcare to e-commerce and beyond. · Data Security and Privacy: Explore best practices for data security, encryption, access control, and compliance to protect sensitive information. Who This Book Is For: "Mastering Data Storage and Processing" is designed for data engineers, developers, analysts, and anyone passionate about effective data management. Whether you're aiming to enhance your skills or embark on a journey toward becoming a data management expert, this book provides the insights and tools to navigate the complexities of data storage and processing. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com
Author: Ron Legarski
Publisher: SolveForce
ISBN:
Category : Technology & Engineering
Languages : en
Pages : 567
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Book Description
"Semiconductors and Superconductors: From Invention to Innovation" is a comprehensive exploration of the fundamental technologies that power modern electronics, energy systems, and computing. Written by Ron Legarski, a leading expert in telecommunications and technology solutions, this book delves into the discovery, evolution, and future applications of semiconductors and superconductors—two cornerstones of modern science and engineering. The book is designed for a wide audience, from professionals in the tech industry and academic researchers to students and general readers interested in understanding the science and technology that drive today’s digital world. Semiconductors are the building blocks of every microchip, transistor, and integrated circuit—essential components in everything from smartphones to solar cells. Superconductors, on the other hand, have the potential to revolutionize fields like energy transmission, quantum computing, and medical imaging by enabling technologies that operate with zero electrical resistance. This book covers the key milestones in the development of semiconductors and superconductors, starting with the invention of the transistor and the discovery of superconductivity. It also dives into the applications of these technologies in industries such as telecommunications, computing, energy systems, and medical technology, demonstrating their far-reaching impact on society. Key topics include: The physics of semiconductors and superconductors, explained in accessible language. The history and evolution of transistors, integrated circuits, and quantum devices. How superconducting materials are used in applications ranging from MRI machines to high-speed trains. The role of semiconductors in smartphones, AI systems, and energy-efficient power grids. Future research directions, including the pursuit of room-temperature superconductors and wide-bandgap semiconductors like SiC and GaN. The convergence of AI, machine learning, and nanotechnology in designing next-generation semiconductor and superconductor devices. The book also provides a forward-looking perspective on how these technologies will shape the future, particularly in fields like quantum computing, artificial intelligence, and renewable energy systems. With chapters organized for easy navigation, technical glossaries, and suggested reading for further exploration, "Semiconductors and Superconductors: From Invention to Innovation" is an essential resource for anyone looking to understand the technological forces that are driving the world forward.
Author: Akshay Krishna Ramanathan
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
With the dominance of data-intensive workloads and applications, the current von-Neumann-based architectures suffer from memory bandwidth problems, popularly known as the "memory wall". In order to alleviate the problem of memory bandwidth, processing-in-memory (PiM) has gained a lot of attention in recent years. In the PiM architectures, the compute logics are moved closer to or within the memory where the data resides, enabling the PiM architectures to exploit the high internal bandwidth of the memories. This dissertation explores the opportunities provided by the recent advancements in-memory technologies to design highly efficient PiM architectures for mainly deep-learning, database, and persistence applications. The first work in this dissertation presents a novel 3D-SRAM circuit design using a Monolithic 3D Integration process (M3D) for realizing beyond-Boolean in-memory compare operation without any area overheads compared to the standard 6T-SRAM. We also showcase measurement results from the fabricated PiM macro with the same circuit design for performing massively parallel compare operation used in the database, machine learning, and scientific applications. The proposed PiM technique supports operations like data filtering, sorting, and index handling of sparse matrix-matrix multiplication (SpGEMM). The second work presents a Look-Up Table (LUT) based PiM technique for conventional SRAM memory technology (i.e., single layer) with the potential for running Neural Network inference tasks. We implement a bitline computing free technique to avoid frequent bitline accesses to the cache sub-arrays and thereby considerably reducing the memory access energy overhead. Our proposed LUT-based PiM methodology exploits substantial parallelism using look-up tables, which do not alter the memory structure/organization. This methodology showcases a PiM architecture for current memory technologies with minimal changes to the monolithic custom memory blocks. The third work deals with crash consistency for critical applications like financial trading, cyber threat analysis, IoT, etc. At present, non-volatile memory technologies promise the opportunity for maintaining persistent data in memory. However, providing crash consistency in such systems can be costly as any update to the persistent data has to reach the persistent hard drive in a specific order, imposing a high overhead. In this work, we propose an architecture design that employs a hybrid volatile, non-volatile memory cell employing M3D and Ferroelectric technology in the L1 data cache to guarantee crash consistency with almost no performance overhead. Memory technologies like high bandwidth memory (HBM), and solid-state drives (SSD) make use of parallel-3D integration process to stack memory layers in order to increase the density per mm2. The final work presents cost-effective potential N-layer logic designs realized by the same process. This work discusses the stricter rules and constraints enforced by the fabrication process when designing N-layer designs and then explores different adder designs.
Author: Swaroop Ghosh
Publisher: Springer
ISBN: 3319973479
Category : Technology & Engineering
Languages : en
Pages : 116
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Book Description
This book introduces readers to the latest advances in sensing technology for a broad range of non-volatile memories (NVMs). Challenges across the memory technologies are highlighted and their solutions in mature technology are discussed, enabling innovation of sensing technologies for future NVMs. Coverage includes sensing techniques ranging from well-established NVMs such as hard disk, flash, Magnetic RAM (MRAM) to emerging NVMs such as ReRAM, STTRAM, FeRAM and Domain Wall Memory will be covered.
Author: Baker Mohammad
Publisher: Springer Nature
ISBN: 303134233X
Category : Technology & Engineering
Languages : en
Pages : 145
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Book Description
This book describes the state-of-the-art of technology and research on In-Memory Computing Hardware Accelerators for Data-Intensive Applications. The authors discuss how processing-centric computing has become insufficient to meet target requirements and how Memory-centric computing may be better suited for the needs of current applications. This reveals for readers how current and emerging memory technologies are causing a shift in the computing paradigm. The authors do deep-dive discussions on volatile and non-volatile memory technologies, covering their basic memory cell structures, operations, different computational memory designs and the challenges associated with them. Specific case studies and potential applications are provided along with their current status and commercial availability in the market.
Author: Samee U. Khan
Publisher: Springer
ISBN: 1493920928
Category : Computers
Languages : en
Pages : 1309
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Book Description
This handbook offers a comprehensive review of the state-of-the-art research achievements in the field of data centers. Contributions from international, leading researchers and scholars offer topics in cloud computing, virtualization in data centers, energy efficient data centers, and next generation data center architecture. It also comprises current research trends in emerging areas, such as data security, data protection management, and network resource management in data centers. Specific attention is devoted to industry needs associated with the challenges faced by data centers, such as various power, cooling, floor space, and associated environmental health and safety issues, while still working to support growth without disrupting quality of service. The contributions cut across various IT data technology domains as a single source to discuss the interdependencies that need to be supported to enable a virtualized, next-generation, energy efficient, economical, and environmentally friendly data center. This book appeals to a broad spectrum of readers, including server, storage, networking, database, and applications analysts, administrators, and architects. It is intended for those seeking to gain a stronger grasp on data center networks: the fundamental protocol used by the applications and the network, the typical network technologies, and their design aspects. The Handbook of Data Centers is a leading reference on design and implementation for planning, implementing, and operating data center networks.
