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

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.