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NOBEL PRIZE 2026

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NSFDirectorate for Computer and Information Science and Engineering

CAREER: Enabling Memory-Centric Computing from Internet of Things to Cloud

Active
Institution
University of California-Riverside, CA
Principal investigator
Elaheh Sadredini
Funding
$588K
Duration
2024–2029
Open portfolio

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Still being scored

Abstract

Today's computing landscape is predominantly driven by data-centric applications, where their performance heavily relies on efficient data access and processing. Memory-centric computing revolutionizes data processing by performing computations in close proximity to the locations where data is generated/captured or stored, such as sensors, memory, and storage devices, and capitalizes on the extensive parallelism inherent in memory arrays. Therefore, it fundamentally tackles the data movement and instruction processing bottleneck, offering a powerful solution for data-intensive workloads in domains like machine learning and genomics, with far-reaching implications for scientific advancements. The overarching goal of this project is to facilitate the paradigm shift from compute-centric to memory-centric systems and seamlessly and securely integrate them into today's modern systems. This transformation will enable efficient and secure computation, fostering advancements in data processing capabilities. This project aims to integrate the acquired methodologies and insights into age-appropriate curricula, from K-12 to graduate-level courses. It also incorporates several STEM (science, technology, engineering and mathematics) enrichment activities for local K-12 and community college students in the Inland Empire. This award aims to facilitate the integration of the memory-centric computing paradigm into present-day computing systems. To fully exploit the potential of memory-centric computing, this project takes a holistic approach by co-designing the entire computing stack to facilitate the adoption of near-data processing in modern architectures. It will also investigate the security and privacy challenges posed by memory-centric computing solutions and propose lightweight techniques to effectively address these challenges. The developed frameworks and tools will be made publicly available to benefit the computer science community and to serve as the foundation for new course development and creating research opportunities for undergraduate and high school students. 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.

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Directorate for Computer and Information Science and EngineeringWOMEN, MINORITY, DISABLED, NECSoftware & Hardware FoundationCAREER-Faculty Erly Career DevCOMPUTER ARCHITECTUREprocessingadvancementstodaycomputingdata processingdata movementparallelismdata-centric

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