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

随着社交媒体和高清视频流的出现，人们越来越需要一种更高效的方式来处理带宽和能量方面的可视信息流。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改变。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定时驱动，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、传感方面的革命性进展，并在自动驾驶车辆、神经假肢、机器人和一般人工智能中得到主要应用。该项目团队将与当地社区密切合作，通过包括实验室演示、暑期实习和职业研讨会在内的外联活动，培养人们对神经形态计算和人工智能的兴趣，从而鼓励来自各种背景的学生参与计算职业生涯，包括代表不足的群体。这个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)相结合来构建一个大脑启发的视觉系统。在大多数现有的神经形态视觉系统中，基于事件的摄像机与计算系统之间的通信仍然受到存储瓶颈的限制，这在很大程度上否定了神经形态摄像机的大带宽和低功耗的优点。该项目将：(1)构建一个具有现实的基于石墨烯的动态突触的尖峰神经网络，从而允许高级计算能力；(2)开发受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，以展示具有数量级更高能效和带宽的实时操作视觉系统。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.