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

CRII：CNS：设计和系统技术协同优化解决深度学习硬件的内存瓶颈问题

• 批准号: 2153394
• 负责人: Mehdi Sadi
• 金额: $ 17.49万
• 依托单位: Auburn University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2153394&HistoricalAwards=false
• 关键词: CRII; CNS; Design; System; Technology

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.

该奖项是根据2021年《美国救援计划法》（公法117-2）的全部或部分资助的。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，机器人技术，基因编辑，气候科学，气候科学以及许多其他领域。数据大小正在显着增加，产生较大的数据集和模型尺寸，以达到所需的AI/DL准确性水平。在过去的几年中，AI计算能力的增长远远超过了芯片和外片的每个加速器记忆能力的增长。内存已成为AI/DL硬件中的关键瓶颈，要求采用新的方法来解决这种瓶颈。该项目包括两个关键推力：（1）考虑到设计与技术（DTCO）之间的交互以及整体系统和技术（STCO），考虑到片上和芯片内存储系统的关键性能参数将与AI/DL硬件进行优化。 (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.随着Moore定律的回报率降低，STCO和DTCO最近已成为新兴的范式，以调整技术以获得硬件的最佳性能增长。这项工作的结果将有助于丰富该领域的科学知识，并影响从事其他新兴技术领域的未来研究人员。符合目标在AI/DL领域建立美国领导的目标，该项目的努力致力于通过研究生和本科研究来实现卓越的教育，劳动力发展和宣传卓越，精神上缺乏代表性的学生和少数派学生，并通过K-12级的Internifection Insport Internifection Internition Internifection Internife Internifection nsf decrient of Derem nsf的宣布，以表现出nsf的宣布。优点和更广泛的影响审查标准。

项目成果

System and Design Technology Co-optimization of Chiplet-based AI Accelerator with Machine Learning

基于Chiplet的AI加速器与机器学习的系统和设计技术协同优化

• DOI: 10.1145/3583781.3590233
• 发表时间: 2023
• 期刊: Proceedings of the Great Lakes Symposium on VLSI
• 作者: Mishty, Kaniz;Sadi, Mehdi
• 通讯作者: Sadi, Mehdi

Designing Efficient and High-Performance AI Accelerators With Customized STT-MRAM

• DOI: 10.1109/tvlsi.2021.3105958
• 发表时间: 2021-10-01
• 期刊: IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS
• 影响因子: 2.8
• 作者: Mishty, Kaniz;Sadi, Mehdi
• 通讯作者: Sadi, Mehdi

Analogy-Guided Evolutionary Pretraining of Binary Word Embeddings

类比引导的二进制词嵌入进化预训练

• 发表时间: 2022
• 期刊: Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers
• 作者: R. Alexander Knipper, Md. Mahadi