CAREER: Leveraging physical properties of modern flash memory chips for resilient, secure, and energy-efficient edge storage systems

人和机器创建、捕获、复制和使用的数据呈指数级增长，创造了ZB(10亿TB)时代，对未来的数据存储平台提出了新的挑战。为了跟上不断增长的数据存储需求，未来的存储解决方案将需要提供更高的位密度、更高的性能和能效以及更高的弹性，同时保证最终用户的隐私。应对这些挑战需要存储系统设计方法的范式转变，而不是传统的与技术无关的算法方法。为了实现这一目标，该项目将创造新的技术，利用存储介质的丰富物理属性，从而实现非传统但高效的存储系统。具体地说，该项目(1)开发新的特定于技术的表征技术以探索闪存在运行时的物理属性，(2)通过闪存的逻辑扩展来探索新的自适应存储管理技术，以及(3)提高我们对基本存储器阵列特征的知识，例如电子噪声、工艺变化、单元退化以及耐久性和能量精度权衡的极限。此外，该项目还探索了新的数据编码概念，例如分数位存储、基于非电荷的数据编码和节能的近似存储技术，这些技术对于边缘新兴的数据密集型应用至关重要。这些技术旨在确保广泛的适用性、便携性和易于实施，承诺增强各种未来存储解决方案的弹性、安全性和能效。该项目将在包括边缘/云数据中心和个人存储设备在内的广泛存储应用中为消费者、行业和美国政府带来重大好处。这些技术将使系统集成商能够在极端环境(例如，核和太空)中运行的边缘节点开发近期破坏性数据密集型应用程序，例如人工智能和预测分析。此外，还将利用行业合作，对该项目中开发的任何相关技术进行批判性反馈和可能的商业化。该教育和推广计划包括在网络安全和先进存储技术领域对研究生和本科生进行培训，举办年度研讨会和夏令营以吸引年轻学生，特别是来自代表不足的群体的学生。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Exponential growth in data created, captured, copied, and consumed by people and machines has created the Zettabytes (one billion terabytes) era, posing new challenges to the future data storage platforms. To keep up with growing demands for data storage, future storage solutions will need to provide even higher bit densities, superior performance and energy-efficiency, and improved resilience, while guaranteeing end-user privacy. Addressing these challenges requires a paradigm shift in the storage system design methods beyond the traditional technology-agnostic algorithmic approaches. To achieve this goal, the project will create new techniques that exploit rich physical properties of storage media, thus enabling unconventional but highly effective storage systems. Specifically, the project (1) develops new technology-specific characterization techniques to explore physical properties of flash memory during run time, (2) explores new adaptive storage management techniques through logical scaling of flash memory, and (3) advances our knowledge of fundamental memory-array characteristics such as electronic noise, process variation, cell degradation, and the limits of endurance and energy-accuracy trade-offs. In addition, this project explores novel data-encoding concepts such as fractional bit storage, non-charge based data encoding, and energy-efficient approximate storage techniques that will be critical for the emerging data-intensive applications at the edge. These techniques are tailored to ensure wide applicability, portability and ease of implementation promising to enhance resilience, security, and energy-efficiency of a wide range of future storage solutions.The project will provide significant benefits to consumers, industry, and U.S. government alike in a wide range of storage applications including edge/cloud data centers and personal storage devices. These techniques will enable system integrators to develop near-term disruptive data-intensive applications such as artificial intelligence and predictive analytics at the edge nodes operating in extreme environments (e.g., nuclear and space). In addition, industrial collaborations will be leveraged for critical feedback and possible commercialization of any relevant techniques developed in this project. The educational and outreach plan includes training graduate and undergraduate students in the area of cybersecurity and advanced storage technologies, conducting annual workshop and summer camps to attract young students, particularly from underrepresented groups.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.