CRII: CNS: Design and System Technology Co-optimization Towards Addressing the Memory Bottleneck Problem of Deep Learning Hardware

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This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Artificial intelligence and deep learning (AI/DL) are influencing a range of areas, including autonomous vehicles, healthcare, cybersecurity, language processing, robotics, gene editing, climate science, and numerous others. Data size is increasing significantly, yielding ever larger data sets and model sizes to achieve desired levels of AI/DL accuracy. Over the last several years, growth in AI compute capability has far exceeded growth in per-accelerator memory capacity, both on-chip and off-chip. Memory has become the key bottleneck in AI/DL hardware, demanding new approaches to resolve this bottleneck. This project includes two key thrusts: (1) Key performance parameters of on-chip and off-chip memory systems will be co-optimized with AI/DL hardware, considering interactions between the Design and Technology (DTCO), and the overall System and Technology (STCO). (2) Emerging Magnetic Random Access Memory (MRAM), chiplets, and packaging interconnect technologies will be utilized to optimally design the hardware.This project will influence novel paradigms for designing high-performance and energy-efficient AI/DL hardware, impacting the development of new AI/DL algorithms – bringing society one step closer to achieving human-level intelligence in machines. With diminishing returns from Moore’s law, STCO and DTCO have recently become emerging paradigms for tuning the technology for the best performance gains in hardware. The outcomes of this work will be instrumental in enriching scientific knowledge in this field and influence future researchers working on other emerging technical domains. Aligned with the goal of establishing United States’ leadership in the AI/DL domain, the efforts of this project are dedicated to achieving excellence in education, workforce development, and outreach through graduate and undergraduate research, mentoring underrepresented and minority students, and promoting AI hardware education at the K-12 level.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.