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

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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.