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

该奖项全部或部分由2021年美国救援计划法案（公法117-2）资助。人工智能和深度学习（AI/DL）正在影响一系列领域，包括自动驾驶汽车、医疗保健、网络安全、语言处理、机器人技术、基因编辑、气候科学等。数据量正在显著增加，产生更大的数据集和模型大小，以实现所需的AI/DL准确度水平。在过去的几年里，人工智能计算能力的增长远远超过了每个加速器内存容量的增长，无论是片上还是片外。内存已经成为AI/DL硬件的关键瓶颈，需要新的方法来解决这个瓶颈。该项目包括两个关键目标：（1）考虑到设计与技术（DTCO）和整体系统与技术（STCO）之间的相互作用，片上和片外存储系统的关键性能参数将与AI/DL硬件共同优化。(2)新兴的磁性随机存取存储器（MRAM）、小芯片和封装互连技术将被用于优化硬件设计，该项目将影响设计高性能和节能AI/DL硬件的新范式，影响新AI/DL算法的开发，使社会更接近机器实现人类水平的智能。随着摩尔定律的收益递减，STCO和DTCO最近已成为调整技术以获得硬件最佳性能增益的新兴范例。这项工作的成果将有助于丰富这一领域的科学知识，并影响未来在其他新兴技术领域工作的研究人员。与建立美国在AI/DL领域的领导地位的目标相一致，该项目的努力致力于通过研究生和本科生的研究，指导代表性不足的学生和少数民族学生，实现卓越的教育，劳动力发展和推广，并在K-12级。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.