Collaborative Research: High Performance Cellular Simultaneous Recurrent Network based Pattern Recognition

合作研究：基于高性能蜂窝同时循环网络的模式识别

• 批准号: 1309708
• 负责人: Tarek Taha
• 金额: $ 14.66万
• 依托单位: University of Dayton
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-06-01 至 2017-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1309708&HistoricalAwards=false
• 关键词: Collaborative; Research; Performance; Cellular; Simultaneous

This is a collaborative proposal between a neural network researcher, addressing the issues of face recognition and image recognition in general, and a researcher on a new class of electronic chip based on memristors. In recent years, new world records have been set in image recognition by convolutional neural networks, funded at other universities through the EFRI/COPN topic at NSF. At times, those systems have outperformed humans in those tasks. On the neural network side, this team plans to use a more general class of neural networks, the Cellular Simultaneous Neural Network (CSRN), to address benchmark challenges in face recognition where computers have yet to outperform humans. The CSRN may be viewed asa generalization of the convolutional network to add a kind of real-time recurrence or feedback, a kind of recurrence which is known to be crucial to the powers of biological brains. On the electronic hardware side, this proposal addresses a crucial challenge in continuing Moore's Law. The speed of computing chips is not expected to grow as fast as it did in the past, but thanks to breakthroughs in lithography and the recent work in memristors, we can still expect progress towards thousand or even millions of active processors on a chip. In order to make full use of this emerging new capability, new efforts are needed to integrate device work and systems level work together, in developing new architectures of real practical use. If successful, this project could be an important step forward in that effort. Memristors for use in memory are already being well-funded by industry, but the extension to active processing and learning is more of a high riskbreakthrough activity. This project also includes a substantial component of education and outreach, including development of systems to stimulate K-8 children.

这是神经网络研究人员之间的一个协作建议，解决了面部识别和图像识别的问题，以及基于Memristors的新电子芯片的研究人员。 近年来，卷积神经网络已通过NSF的EFRI/COPN主题在其他大学资助。有时，这些系统在这些任务中表现优于人类。在神经网络方面，该团队计划使用更通用的神经网络，即蜂窝同时神经网络（CSRN），以应对面部识别中的基准挑战，这些挑战尚未超过人类的表现。可以将CSRN视为卷积网络的ASA概括，以添加一种实时复发或反馈，这是一种已知对生物学大脑的力量至关重要的复发。 在电子硬件方面，该提案解决了继续摩尔定律的关键挑战。预计计算芯片的速度不会像过去那样快地增长，但是由于光刻的突破和最近在备忘录中的工作的突破，我们仍然可以期望在芯片上朝着数千甚至数百万个活跃的处理器方面的进步。为了充分利用这种新兴的新能力，需要进行新的努力来集成设备的工作和系统级别共同努力，以开发新的实际实用架构。如果成功的话，这个项目可能是这项努力迈出的重要一步。用于记忆的备忘录已经被行业资金充分资金，但是积极的处理和学习的扩展更像是一项高风险的活动。 该项目还包括教育和外展的重要组成部分，包括开发刺激K-8儿童的系统。