Architectural Techniques to Sustain Moore's Law to Enable Reliable, Secure, and Efficient Memory Systems

维持摩尔定律的架构技术，实现可靠、安全和高效的内存系统

• 批准号: RGPIN-2019-05059
• 负责人: Nair, PrashantJayaprakash
• 金额: $ 2.04万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=752211
• 关键词: Architectural; Techniques; Sustain; Moore; Law

Moore's law has been instrumental in enabling scalable high-capacity memory systems. Unfortunately, sustaining Moore's law is challenging, as scaling the feature sizes of memory cells also reduces their reliability and makes them insecure. The long-term mission of this research is to investigate clever schemes that enable reliable and secure memory systems for a wide range of applications across different computing systems. Our research will significantly enhance energy efficiency, reduce energy costs, and help build high-performance computing systems. This work will enable Canadians to develop computing systems that provide significant economic and environmental gains. On a technological front, enabling secure and reliable high-capacity memory systems is essential for building data centers, smartphones, desktops, and supercomputers. Furthermore, these systems are instrumental in enabling Canada to be a leader in cloud computing, machine learning and artificial intelligence, commerce, and scientific computing. To this end, this program aims to provide three main research themes. 1. Computer hardware and architecture for fault tolerance: To help sustain Moore's law by improving memory reliability by using architecture and system-wide strategies. Currently, memory systems distribute data across several memory dies and keep error-codes to correct faulty data. Our program will go beyond these approaches and investigate cross-layer schemes, data redundancy strategies, new memory technologies, cell variability, and page mapping schemes to improve reliability further. 2. Security and Privacy: In this era of big data, machine learning and artificial intelligence, memory systems are increasingly being used to store and provide sensitive data. As the reliability of memory systems reduces, it is becoming even more challenging to store data securely. To make matters worse, as current architecture and system-level solutions tend to have significant performance and power overheads. Our program will look at architecture and systems-level solutions that will enable high security at low-costs. 3. Data Management: As memory capacity increases, its bandwidth also needs to grow proportionally. This program will investigate data compression strategies to enhance bandwidth seamlessly. Data compression in current systems tends to require massive software support and also incurs additional hardware overheads. Our program will explore low-cost data compression strategies at architecture and system-level for main memory, caches, and storage. Development of highly qualified personnel (HQP) is a critical requirement for Canada. One of the critical goals of this program is to train HQP to develop vital skills in the areas of computer architecture, statistics and probability theory, mathematical analysis, performance analysis, operating systems, compilers, and programming languages.

摩尔定律在实现可扩展的大容量存储系统方面发挥了重要作用。不幸的是，维持摩尔定律具有挑战性，因为缩小存储单元的特征尺寸也会降低其可靠性并使其不安全。这项研究的长期使命是研究巧妙的方案，为跨不同计算系统的广泛应用提供可靠和安全的内存系统。我们的研究将显着提高能源效率，降低能源成本，并帮助构建高性能计算系统。这项工作将使加拿大人能够开发出可带来显着经济和环境效益的计算系统。在技​​术方面，实现安全可靠的大容量存储系统对于构建数据中心、智能手机、台式机和超级计算机至关重要。此外，这些系统有助于加拿大成为云计算、机器学习和人工智能、商业和科学计算领域的领导者。为此，该计划旨在提供三个主要研究主题。 1. 用于容错的计算机硬件和架构：通过使用架构和系统范围的策略来提高内存可靠性，从而帮助维持摩尔定律。目前，存储系统将数据分布在多个存储芯片上，并保留错误代码以纠正错误数据。我们的计划将超越这些方法，并研究跨层方案、数据冗余策略、新内存技术、单元可变性和页面映射方案，以进一步提高可靠性。 2.安全和隐私：在这个大数据、机器学习和人工智能的时代，存储系统越来越多地被用来存储和提供敏感数据。随着存储系统的可靠性降低，安全存储数据变得更加困难。更糟糕的是，当前的架构和系统级解决方案往往具有显着的性能和功耗开销。我们的计划将着眼于架构和系统级解决方案，以低成本实现高安全性。 3.数据管理：随着内存容量的增加，其带宽也需要成比例增长。该计划将研究数据压缩策略以无缝增强带宽。当前系统中的数据压缩往往需要大量软件支持，并且还会产生额外的硬件开销。我们的计划将探索主内存、缓存和存储的架构和系统级别的低成本数据压缩策略。培养高素质人才（HQP）是加拿大的一项关键要求。该计划的关键目标之一是培训总部人员发展计算机体系结构、统计和概率论、数学分析、性能分析、操作系统、编译器和编程语言等领域的重要技能。