SHF: Small: Harnessing human intelligence for mapping on custom reconfigurable architectures

SHF：小型：利用人类智能在自定义可重构架构上进行映射

• 批准号: 1218656
• 负责人: Gayatri Mehta
• 金额: $ 49.99万
• 依托单位: University of North Texas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-07-01 至 2017-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1218656&HistoricalAwards=false
• 关键词: SHF; Small; Harnessing; human; intelligence

Custom domain-specific architectures have great promise for achieving energy-efficient flexible designs for a suite of applications. For practical deployment, however, there is a great need to develop smart algorithms for mapping applications of interest onto these architectures. The main thrust of this research is to discover novel mapping algorithms by making use of human intuition and ability to recognize patterns and opportunities even in complex problems. Participants are presented with successively more difficult mapping problems in a game environment, and the vast dataset of participants' moves is analyzed to recognize common patterns used by successful game participants. The insights gained from strategic moves humans make while solving problems based on their visual intuition and experience can be used to discover new mapping approaches that are beyond what can be conceived with traditional algorithms. For broad scope and impact, this research places particular attention on multiple architectural designs, highly constrained (and thus very difficult) mapping problems, and engaging a broad community of contributors through crowdsourcing.Fast and effective mapping techniques, and a methodology for developing new such techniques as needed has the potential to inspire architectural innovation and result in production of devices that are faster, cheaper, and smarter. Results from this research can advance next generation portable/wearable devices critical to health, safety and security, personal multimedia, and aerospace.

定制的特定于领域的架构对于为一系列应用程序实现节能灵活设计具有巨大的希望。然而，对于实际部署，非常需要开发智能算法来将感兴趣的应用程序映射到这些架构上。本研究的主旨是利用人类的直觉和能力，即使在复杂的问题中识别模式和机会，发现新的映射算法。参与者在游戏环境中遇到越来越困难的映射问题，并分析参与者移动的大量数据集，以识别成功游戏参与者使用的常见模式。人类在基于视觉直觉和经验解决问题时所采取的战略行动所获得的洞察力可以用来发现新的映射方法，这些方法超出了传统算法的设想。由于研究范围和影响广泛，本研究特别关注高度受限的多种建筑设计（因此非常困难）映射问题，并通过众包吸引广泛的贡献者社区。快速有效的映射技术，以及根据需要开发新的此类技术的方法，有可能激发建筑创新，并导致生产更快，更便宜，更聪明这项研究的结果可以推动下一代便携式/可穿戴设备的发展，这些设备对健康、安全和安保、个人多媒体和航空航天至关重要。