SHF: Small: Reconfigurability and Technology Integration of Magnetic Energy Minimization Co-Processor (MEMCoP)

SHF：小型：磁能最小化协处理器 (MEMCoP) 的可重构性和技术集成

• 批准号: 1619027
• 负责人: Sanjukta Bhanja
• 金额: $ 45万
• 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: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2020-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1619027&HistoricalAwards=false
• 关键词: SHF; Small; Reconfigurability; Technology; Integration

This collaborative proposal been two universities - USF and UCLA - advocates a new form of computing that uses circular nanomagnets to solve quadratic optimization problems, orders of magnitude faster than that of a conventional computer. A wide range of application domains can be potentially accelerated through this research, e.g., computationally expensive and hard to parallelize problems of finding patterns in social media, error-correcting codes, big data applications etc. The project plans to engage undergraduate REU students, and the PIs will be involved with the local community through interaction with K-12 teachers and K7-K8 women students. As a part of Sloan University Center for Exemplary Mentoring (UCEM), the lead PI will recruit and retain undergraduate students. The UCLA team will work with a group of undergraduate students sponsored by Center for Excellence in Engineering and Diversity (CEED).Core computational theme of this proposal is mapping quadratic energy minimization problem spaces into a set of interacting magnets such that the energy relationship between the problem variables is proportional to that of the energies between the corresponding magnets. The optimization is accomplished by the relaxation physics of the magnets themselves and solutions can be read-out in parallel. In essence, given a specific instance of the problem, the plan is to arrive at a specific magnetic layout, the relaxed state of which will be the solution of the original problem. Reconfigurability is fundamental to the success of this form of high-payoff computing (one does not intend to fabricate a specific magnetic layout to solve one instance of a problem). Three alternative programming solutions are proposed, and trade-offs between the mechanisms will be evaluated. The project also plans to explore reconfigurability together with readability and system integration aspects.

这项合作提案由两所大学- USF和UCLA -倡导一种新的计算形式，使用圆形纳米磁体来解决二次优化问题，比传统计算机快几个数量级。通过这项研究，可以潜在地加速广泛的应用领域，例如，该项目计划让REU的本科生参与，而PI将通过与K-12教师和K7-K8女学生的互动参与当地社区。作为斯隆大学示范指导中心（UCEM）的一部分，首席PI将招募和留住本科生。加州大学洛杉矶分校的团队将与一组由卓越工程和多样性中心（CEED）赞助的本科生合作，该提案的核心计算主题是将二次能量最小化问题空间映射到一组相互作用的磁体中，使得问题变量之间的能量关系与相应磁体之间的能量关系成比例。优化是通过磁体本身的弛豫物理来完成的，并且可以并行地读出解决方案。从本质上讲，给定问题的一个具体实例，该计划是达到一个特定的磁布局，其松弛状态将是原始问题的解决方案。 可重构性是这种高回报计算形式成功的基础（人们不打算制造特定的磁性布局来解决问题的一个实例）。提出了三种替代方案的解决方案，并将评估机制之间的权衡。该项目还计划探索可重构性以及可读性和系统集成方面。