PI: Ryad Benosman
Co-PI: Feng Xiong
Title: FET: Small: Neuromorphic Spiking Neural Networks with Dynamic Graphene Synapses for Event-based Computation
Description: 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.
Source: National Science Foundation
Term: July 1, 2019 – June 30, 2022 (Estimated)