CCRI: ABR: Cognitive Hardware and Software Ecosystem Community Infrastructure (CHASE-CI)

CCRI：ABR：认知硬件和软件生态系统社区基础设施 (CHASE-CI)

• 批准号: 2100237
• 负责人: Larry Smarr
• 金额: $ 100万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-15 至 2023-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2100237&HistoricalAwards=false
• 关键词: CCRI; ABR; Cognitive; Hardware; Software

This project, called the Cognitive Hardware And Software Ecosystem Community Infrastructure (CHASE-CI), is to continue and expand a cloud of hundreds of affordable Graphics Processing Units (GPUs), networked together with a variety of neural network machines to facilitate development of next generation cognitive computing. This cloud will be accessible by 30 researchers assembled from 10 universities via the NSF-funded Pacific Research Platform. These researchers will investigate a range of problems from image and video recognition, computer vision, contextual robotics to cognitive neurosciences using the cloud to be purpose-built in this project.Training of neural network with large data-sets is best performed on GPUs. Lack of availability of affordable GPUs and lack of easy access to the new generation of Non-von Neumann (NvN) machines with embedded neural networks impede research in cognitive computing. The purpose-built cloud will be available over the network to address this bottleneck. PIs will study various Deep Neural Network, Recurrent Neural Network, and Reinforcement Learning Algorithms on this platform.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.

该项目称为认知硬件和软件生态系统社区基础架构（Chase-CI），是继续并扩大数百个负担得起的图形处理单元（GPU）的云，与各种神经网络机器一起建立了网络，以促进下一步的开发一代认知计算。通过NSF资助的太平洋研究平台从10所大学组装的30名研究人员将可以接触这一云。这些研究人员将研究从图像和视频识别，计算机视觉，上下文机器人技术到使用云的认知神经科学的一系列问题。缺乏负担得起的GPU的可用性，并且无法轻松访问具有嵌入式神经网络的新一代非Von Neumann（NVN）机器阻碍认知计算中的研究。该专用云将通过网络可用，以解决此瓶颈。 PIS将在此平台上研究各种深层神经网络，经常性神经网络以及强化学习算法。该奖项反映了NSF的法定任务，并使用基金会的智力优点和更广泛的影响评估审查标准，被认为值得通过评估来获得支持。

项目成果

Revisiting HyperDimensional Learning for FPGA and Low-Power Architectures

• DOI: 10.1109/hpca51647.2021.00028
• 发表时间: 2021-01-01
• 期刊: 2021 27TH IEEE INTERNATIONAL SYMPOSIUM ON HIGH-PERFORMANCE COMPUTER ARCHITECTURE (HPCA 2021)
• 作者: Imani, Mohsen;Zou, Zhuowen;Rosing, Tajana
• 通讯作者: Rosing, Tajana

A Science-Enabled Virtual Reality Demonstration to Increase Social Acceptance of Prescribed Burns

科学支持的虚拟现实演示可提高社会对规定烧伤的接受度

• DOI: 10.1109/escience55777.2022.00072
• 发表时间: 2022
• 期刊: 2022 IEEE 18th International Conference on e-Science (e-Science
• 作者: Nealey, Isaac;Pacheco, Daniela Encinas;Moreno, Ivannia Gomez;Floca, Melissa;Crawl, Daniel;Altintas, Ilkay
• 通讯作者: Altintas, Ilkay

Accelerating Key Bioinformatics Tasks 100-fold by Improving Memory Access

通过改进内存访问将关键生物信息学任务加速 100 倍

• DOI: 10.1145/3437359.3465562
• 发表时间: 2021
• 期刊: Practice and Experience in Advanced Research Computing
• 作者: Sfiligoi, I.

Policy Learning for Active Target Tracking over Continuous SE(3) Trajectories

连续 SE(3) 轨迹上主动目标跟踪的策略学习

• 发表时间: 2023
• 期刊: Learning for Dynamics and Control (L4DC
• 作者: Yang, Pengzhi;Koga, Shumon;Asgharivaskasi, Arash;Atanasov, Nikolay
• 通讯作者: Atanasov, Nikolay

Estimation of wildfire wind conditions via perimeter and surface area optimization

通过周长和表面积优化估计野火风况

• DOI: 10.1016/j.jocs.2022.101633
• 发表时间: 2022
• 期刊: Journal of Computational Science
• 影响因子: 3.3
• 作者: Tan, Li;de Callafon, Raymond A.;Block, Jessica;Crawl, Daniel;Çağlar, Tolga;Altıntaş, Ilkay
• 通讯作者: Altıntaş, Ilkay