FET: Medium: Memory Processing Unit (MPU) - An Efficient, Reconfigurable In-memory Computing Fabric

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Artificial Intelligence (AI) is expected to become a disruptive force for both emerging and mature industry sectors. However, current AI progress is mostly driven by software and algorithm advances, while physical AI implementation is limited by hardware systems that were primarily developed to perform conventional computing tasks. Large scale implementation of AI in smart homes, robotics and autonomous vehicles will only become possible with hardware innovations, through new computing architectures and devices that can overcome the limits of today's systems in terms of power efficiency and speed. This project aims at precisely addressing these problems through the development of a new in-memory computing architecture that is naturally suited for AI applications. Undergraduate and graduate students will be trained to become experts at the interface of nanoelectronic devices and computing architecture, and be able to join the workforce of computer engineering and semiconductor research and development. The knowledge developed in this project will also be widely disseminated to the general public through publications, tutorials, course modules, high-school visits and industry partnerships. The proposed project will lead to a new computing architecture that is fundamentally efficient, parallel, modular and reconfigurable. Unlike specialized accelerators designed for specific algorithms, the project aims at developing a general memory-centric hardware platform that can be used for a broad range of computing tasks. The program will be carried out through multidisciplinary research efforts organized around five central thrusts that cover small scale prototype and circuit verification, uniform module development, scalable chip design, algorithm mapping, and system benchmarking and optimization. Key performance parameters will be measured and optimized, while new devices, circuit components, design tools and simulation packages will be developed and shared with the research community and the general public to help broaden the impact of the project.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.