Phase 1 IUCRC University of Notre Dame: Center for Alternative Sustainable and Intelligent Computing (ASIC)

第一阶段 IUCRC 圣母大学：替代可持续和智能计算中心 (ASIC)

• 批准号: 1822099
• 负责人: Yiyu Shi
• 金额: $ 75万
• 依托单位: University of Notre Dame
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1822099&HistoricalAwards=false
• 关键词: Phase; IUCRC; University; Notre; Dame

Neuromorphic circuits, which mimic the human brain's structure and processing ability on silicon, promise to enable ultralow power, high performance computing. Various key breakthroughs have emerged from exponential advances in computing technology and understanding brains, with increasing interest from governments, research institutes, and business entities. Nonetheless, existing computing architectures can hardly demonstrate universal advantages in a large variety of neuromorphic computing tasks. To address this challenge, this project will build the Center of Alternative Sustainable and Intelligent Computing (ASIC). ASIC is a collaborative industry-university partnership that joins leading industry members with academic researchers to conduct multidisciplinary science and research to enable high performance and energy efficient machine intelligence through novel circuits, architecture, and computation models.The Center of Alternative Sustainable and Intelligent Computing (ASIC) focuses on alternative computing platforms for cognitive applications, which are in general difficult to be efficiently performed on conventional von Neumann architecture. The ASIC Notre Dame (ASIC-ND) site will focus primarily on neuromorphic computing platforms for telemedicine and healthcare related applications, by leveraging the strong research background and the long-term collaboration with Indiana University School of Medicine and relevant industry sectors. The establishment of ASIC is expected to accelerate the development of new computing diagrams in industry sectors and expedites technology transfer from research discoveries to practical solutions.ASIC research will have broad impact on the computer and IT industries by introducing alternative computing platforms that complement the conventional computing platforms built on von Neumann architectures in the short term, and position alternative sustainable and intelligent computing equivalents important in long term. The knowledge and expertise developed via ASIC will effectively enable the predicted boom in cognitive applications and their urgent demands for hardware and software support. The talent and expertise of center PIs and the industry partners are integrated to enhance the competence of the center members and accelerate technology transfer.ASIC research may involve academic and industrial data along with public domain data. This data and the resulting research outputs will be maintained using appropriate best practices for each type of data for a period of three years after the closing of ASIC. A central repository, suitably tagged for appropriate referencing and documentation, will be set up at https://asic.pratt.duke.edu for maintaining the acquired and generated data from center projects. Access to all models and project results will be stored online and made available for downloading in near real time to respond to approved user requests.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.

神经形态电路在硅上模仿人脑的结构和处理能力，有望实现超低功耗、高性能计算。计算技术和大脑理解的指数级进步带来了各种关键突破，引起了政府、研究机构和商业实体越来越大的兴趣。然而，现有的计算架构很难在各种各样的神经形态计算任务中表现出普遍的优势。为了应对这一挑战，该项目将建立替代可持续和智能计算中心（ASIC）。ASIC是一个产学合作伙伴关系，将领先的行业成员与学术研究人员联合起来，进行多学科的科学和研究，通过新颖的电路、架构和计算模型实现高性能和节能的机器智能。替代可持续和智能计算中心（ASIC）专注于认知应用的替代计算平台，这通常难以在传统的冯·诺依曼架构上有效地执行。ASIC Notre Dame（ASIC-ND）网站将主要专注于远程医疗和医疗保健相关应用的神经形态计算平台，利用强大的研究背景以及与印第安纳州大学医学院和相关行业的长期合作。ASIC的建立有望加速工业领域新计算图表的开发，并加速技术从研究发现到实际解决方案的转移。ASIC研究将在短期内引入替代计算平台，补充基于冯诺依曼架构的传统计算平台，从而对计算机和IT行业产生广泛影响。并将替代的可持续和智能计算等同物定位为长期重要的。通过ASIC开发的知识和专业知识将有效地实现认知应用的预期繁荣及其对硬件和软件支持的迫切需求。中心PI和行业合作伙伴的人才和专业知识相结合，以提高中心成员的能力，加速技术转移。ASIC研究可能涉及学术和工业数据以及公共领域数据沿着。这些数据和由此产生的研究成果将在ASIC关闭后的三年内使用适当的最佳实践对每种类型的数据进行维护。将在https://asic.pratt.duke.edu上建立一个中央存储库，适当标记以进行适当的参考和记录，以维护从中心项目获取和生成的数据。访问所有模型和项目结果将被存储在网上，并提供下载在近真实的时间，以回应批准的用户requests.This奖项反映了NSF的法定使命，并已被认为是值得的支持，通过评估使用基金会的知识价值和更广泛的影响审查标准。

项目成果

RADARS: Memory Efficient Reinforcement Learning Aided Differentiable Neural Architecture Search

RADARS：记忆高效强化学习辅助可微神经架构搜索

• DOI: 10.1109/asp-dac52403.2022.9712499
• 发表时间: 2022
• 期刊: IEEE/ACM Asia and South Pacific Design Automation Conference
• 作者: Yan, Zheyu;Jiang, Weiwen;Hu, Xiaobo Sharon;Shi, Yiyu
• 通讯作者: Shi, Yiyu

Co-Exploration of Neural Architectures and Heterogeneous ASIC Accelerator Designs Targeting Multiple Tasks

针对多个任务的神经架构和异构 ASIC 加速器设计的联合探索

• DOI: 10.1109/dac18072.2020.9218676
• 发表时间: 2020
• 期刊: IEEE/ACM Design Automation Conference
• 作者: Yang, Lei;Yan, Zheyu;Li, Meng;Kwon, Hyoukjun;Lai, Liangzhen;Krishna, Tushar;Chandra, Vikas;Jiang, Weiwen;Shi, Yiyu
• 通讯作者: Shi, Yiyu

Distributed contrastive learning for medical image segmentation

用于医学图像分割的分布式对比学习

• DOI: 10.1016/j.media.2022.102564
• 发表时间: 2022
• 期刊: Medical Image Analysis
• 影响因子: 10.9
• 作者: Wu, Yawen;Zeng, Dewen;Wang, Zhepeng;Shi, Yiyu;Hu, Jingtong
• 通讯作者: Hu, Jingtong

Hardware/Software Co-Exploration of Neural Architectures

• DOI: 10.1109/tcad.2020.2986127
• 发表时间: 2020-12-01
• 期刊: IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
• 影响因子: 2.9
• 作者: Jiang, Weiwen;Yang, Lei;Hu, Jingtong
• 通讯作者: Hu, Jingtong

Personalized Neural Network for Patient-Specific Health Monitoring in IoT: A Meta-Learning Approach

物联网中用于患者特定健康监测的个性化神经网络：元学习方法

• DOI: 10.1109/tcad.2022.3162182
• 发表时间: 2022
• 期刊: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
• 影响因子: 2.9
• 作者: Jia, Zhenge;Shi, Yiyu;Hu, Jingtong
• 通讯作者: Hu, Jingtong