Phase I IUCRC Syracuse University: Center for Alternative Sustainable and Intelligent Computing (ASIC)

第一阶段 IUCRC 雪城大学：替代可持续和智能计算中心 (ASIC)

• 批准号: 1822165
• 负责人: Qinru Qiu
• 金额: $ 75万
• 依托单位: Syracuse University
• 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=1822165&HistoricalAwards=false
• 关键词: Phase; IUCRC; Syracuse; University; Center

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. At Syracuse University (SU), the ASIC-SU site focuses on hardware oriented software and algorithm developments for cognitive applications, with an emphasis on autonomous systems, surveillance and unmanned aerial vehicle (UAV) technologies. The establishment of ASIC accelerates the development of new computing paradigms 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。计算技术和理解大脑的指数级进步已经出现了各种关键突破，政府、研究机构和商业实体的兴趣日益浓厚。尽管如此，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 是一种产学合作伙伴关系，将领先的行业成员与学术研究人员联合起来，开展多学科科学研究，通过新颖的电路、架构和计算模型实现高性能和高能效的机器智能。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，这些应用通常难以在传统的冯诺依曼架构上有效地执行。在雪城大学 (SU)，ASIC-SU 站点专注于认知应用的面向硬件的软件和算法开发，重点是自主系统、监控和无人机 (UAV) 技术。 ASIC 的建立加速了工业领域新计算范式的发展，并加快了从研究发现到实际解决方案的技术转移。ASIC 研究将通过引入替代计算平台来补充构建于其上的传统计算平台，从而对计算机和 IT 行业产生广泛影响。冯诺依曼架构在短期内，并在长期内将替代的可持续和智能计算等价物定位为重要。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 和行业合作伙伴的才能和专业知识相结合，以提高中心成员的能力并加速技术转让。ASIC 研究可能涉及学术和工业数据以及公共领域数据。这些数据和由此产生的研究成果将在 ASIC 关闭后三年内使用每种类型数据的适当最佳实践进行维护。将在 https://asic.pratt.duke.edu 建立一个中央存储库，并适当标记以供适当的参考和文档，用于维护从中心项目获取和生成的数据。对所有模型和项目结果的访问将在线存储并可供近实时下载，以响应批准的用户请求。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响进行评估，被认为值得支持审查标准。

项目成果

Approximating Back-propagation for a Biologically Plausible Local Learning Rule in Spiking Neural Networks

尖峰神经网络中生物学上合理的局部学习规则的近似反向传播

• DOI: 10.1145/3354265.3354275
• 发表时间: 2019-07-23
• 期刊: Proceedings of the International Conference on Neuromorphic Systems
• 作者: Amar Shrestha;Haowen Fang;Qing Wu;Qinru Qiu
• 通讯作者: Qinru Qiu

An Event-driven Neuromorphic System with Biologically Plausible Temporal Dynamics

具有生物学上合理的时间动力学的事件驱动的神经形态系统

• DOI: 10.1109/iccad45719.2019.8942083
• 发表时间: 2019-11-01
• 期刊: 2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)
• 作者: Haowen Fang;Amar Shrestha;Ziyi Zhao;Yilan Li;Qinru Qiu
• 通讯作者: Qinru Qiu

An Integrated Simulation Platform for the Analysis of UAS BVLOS Operations Supported by 4G/5G Communications

4G/5G通信支持的无人机超视距运行分析综合仿真平台

• DOI: 10.1109/icns54818.2022.9771502
• 发表时间: 2022-04-05
• 期刊: 2022 Integrated Communication, Navigation and Surveillance Conference (ICNS)
• 作者: Rui Zuo;Zixi Wang;Chen Luo;Carlos Caicedo;M. C. Gursoy;Qinru Qiu;A. Solomon
• 通讯作者: A. Solomon

Multivariate Time Series Classification Using Spiking Neural Networks

使用尖峰神经网络进行多元时间序列分类

• DOI: 10.1109/ijcnn48605.2020.9206751
• 发表时间: 2020-07-01
• 期刊: 2020 International Joint Conference on Neural Networks (IJCNN)
• 作者: Haowen Fang;Amar Shrestha;Qinru Qiu
• 通讯作者: Qinru Qiu

Temporal and Spatial Routing for Large Scale Safe and Connected UAS Traffic Management in Urban Areas

城市地区大规模安全互联无人机交通管理的时空路由

• DOI: 10.1109/rtcsa.2019.8864561
• 发表时间: 2019-08
• 期刊: IEEE 25th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA
• 作者: Zhao, Ziyi;Jin, Zhao;Luo, Chen;Fang, Haowen;Basti, Franco;Gursoy, M. Cenk;Caicedo, Carlos;Qiu, Qinru
• 通讯作者: Qiu, Qinru