FET: Small: Neuromorphic Spiking Neural Networks with Dynamic Graphene Synapses for Event-based Computation

FET：小型：具有动态石墨烯突触的神经形态尖峰神经网络，用于基于事件的计算

• 批准号: 1909797
• 负责人: Feng Xiong
• 金额: $ 50万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1909797&HistoricalAwards=false
• 关键词: FET; Small; Neuromorphic; Spiking; Neural

With the emergence of social media and high-definition video streaming, there is a growing need for a more efficient way to process streams of visual information in terms of both bandwidth and energy. Currently, conventional image sensors record visual information frame by frame, unnecessarily acquiring huge amounts of redundant data since most pixels often may not change from one frame to the next. Inspired by the human brain, this project will develop a neuromorphic vision system, which is driven by the timings of changes in the dynamics of the input signal instead of the conventional image-based stroboscopic acquisition. This work will lead to transformative advances in bio-inspired neuromorphic processing architectures, sensing, with major applications in self-driving vehicles, neural prosthetics, robotics, and general artificial intelligence. The project team will work closely with local communities to encourage participation by students from all backgrounds including underrepresented group in computing careers by fostering interest in neuromorphic computing and artificial intelligence through outreach activities including lab demonstrations, summer internships, and career workshops. The objective of this project is to build a brain-inspired vision system by integrating a neuromorphic, event-based silicon retina with a spiking neural network (SNN). In most existing neuromorphic vision systems, the communication between the event-based camera and the computing system is still limited by the memory bottleneck, largely negating the benefits of the large bandwidth and low power consumption of the neuromorphic camera. This project will: (1) build a spiking neural network with realistic graphene-based dynamic synapses allowing advanced computational capabilities; (2) develop a brain-inspired machine learning and general computation capabilities; (3) connect the developed hardware with a neuromorphic event-based silicon retina to demonstrate real-time operating vision system with orders of magnitude better energy efficiency and bandwidth.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

随着社交媒体和高清视频流的出现，人们越来越需要一种在带宽和能源方面更有效的方式来处理视觉信息流。目前，传统的图像传感器逐帧记录视觉信息，不必要地获取大量冗余数据，因为大多数像素通常不会从一帧到下一帧发生变化。受人脑的启发，该项目将开发一种神经形态视觉系统，该系统由输入信号动态变化的时序驱动，而不是传统的基于图像的频闪采集。这项工作将导致生物启发的神经形态处理架构，传感的变革性进展，主要应用于自动驾驶汽车，神经假肢，机器人和通用人工智能。项目团队将与当地社区密切合作，鼓励来自各种背景的学生参与，包括代表性不足的群体，通过实验室演示，暑期实习和职业研讨会等外展活动培养对神经形态计算和人工智能的兴趣。该项目的目标是通过将神经形态的、基于事件的硅视网膜与尖峰神经网络（SNN）集成来构建一个脑启发的视觉系统。在大多数现有的神经形态视觉系统中，基于事件的相机和计算系统之间的通信仍然受到存储器瓶颈的限制，这在很大程度上否定了神经形态相机的大带宽和低功耗的优点。该项目将：（1）构建具有现实的基于石墨烯的动态突触的尖峰神经网络，允许先进的计算能力;（2）开发大脑启发的机器学习和通用计算能力;（3）将开发的硬件与基于神经形态事件的硅视网膜连接，以展示真实的-时间操作视觉系统，具有数量级更好的能源效率和带宽。该奖项反映了NSF的法定使命，被认为是值得的通过使用基金会的知识价值和更广泛的影响审查标准进行评估来提供支持。