CRI: Infrastructure acquisition for sub-100 nano VLSI research

CRI：100 纳米以下 VLSI 研究的基础设施采购

• 批准号: 0551621
• 负责人: Sanjukta Bhanja
• 金额: $ 21.5万
• 依托单位: University of South Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-03-01 至 2008-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0551621&HistoricalAwards=false
• 关键词: CRI; Infrastructure; acquisition; sub; 100

AbstractProgram: NSF 04-588 CISE Computing Research InfrastructureTitle: CRI: Infrastructure acquisition for sub-100 nano VLSI researchProposal: CNS 0551621PI: Bhanja, SanjuktaInstitution: University of South Florida With increased scaling down of VLSI circuits, into the nano-scale range, the challenges of design of VLSI circuits has grown significantly. This project will acquire computational, storage and software resources to enable the investigators to design tools to handle very large circuits and to perform multi-objective, multi-level designs, to balance across requirements of power, error, and thermal issues. These advanced computational resources will enable the team to explore probabilistic modeling of nano-domain circuits for millions of gates, design and synthesis of large structural descriptions of circuits, stimulus-free model checking for verification of large designs, algorithmic issues in neural network based video coding, analog characterization, and device performance simulation. Broader impacts of the project will include direct training of graduate students and dissemination of tools for synthesis and probabilistic modeling.

AbstractProgram: NSF 04-588 CISE Computing Research InfrastructureTitle: CRI: Infrastructure acquisition for sub-100 nano VLSI researchProposal: CNS 0551621PI: Bhanja, SanjuktaInstitution: University of South Florida With increased scaling down of VLSI circuits, into the nano-scale range, the challenges of design of VLSI电路已经显着增长。该项目将获取计算，存储和软件资源，以使研究人员能够设计工具来处理非常大的电路并执行多目标，多层设计，以平衡功率，错误和热问题的要求。这些先进的计算资源将使团队能够探索纳米障碍电路的概率建模，数以百万计的大门，设计和综合电路的大型结构描述，无刺激的模型检查大型设计的验证，在基于神经网络的视频编码，模拟的神经网络编码，模拟表征和设备性能仿真中的算法问题。该项目的更广泛的影响将包括直接培训研究生以及传播用于合成和概率建模的工具。