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

这是一个神经网络研究人员之间的合作建议，解决了一般的人脸识别和图像识别问题，以及一个基于忆阻器的新型电子芯片的研究人员。 近年来，卷积神经网络在图像识别方面创造了新的世界纪录，该项目由其他大学通过NSF的EFRI/COPN主题资助。有时，这些系统在这些任务中的表现优于人类。在神经网络方面，该团队计划使用更一般的神经网络类别，即细胞同步神经网络（CSRN），来解决人脸识别中的基准挑战，其中计算机尚未超越人类。CSRN可以被视为卷积网络的推广，以添加一种实时递归或反馈，这种递归对生物大脑的能力至关重要。 在电子硬件方面，这一提议解决了延续摩尔定律的关键挑战。计算芯片的速度预计不会像过去那样快速增长，但由于光刻技术的突破和最近在忆阻器方面的工作，我们仍然可以期待在芯片上实现数千甚至数百万个有源处理器的进展。为了充分利用这一新兴的新能力，在开发真实的实用的新体系结构时，需要新的努力来将设备工作和系统级工作集成在一起。如果成功的话，这个项目可能是这一努力的重要一步。用于记忆的忆阻器已经得到了工业界的充分资助，但扩展到主动处理和学习更多的是一个高风险的突破性活动。 该项目还包括教育和外联的重要组成部分，包括开发激励K-8儿童的系统。

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.