Though neuromorphic systems were introduced decades ago, there has been a resurgence of interest in recent years due to the looming end of Moore's law, the end of Dennard scaling, and the tremendous success of AI and deep learning for a wide variety of applications. With this renewed interest, there is a diverse set of research ongoing in neuromorphic computing, ranging from novel hardware implementations, device and materials to the development of new training and learning algorithms. There are many potential advantages to neuromorphic systems that make them attractive in today's computing landscape, including the potential for very low power, efficient hardware that can perform neural network computation. In this presentation, a brief overview of the current state of neuromorphic computing systems will be given, and we will show how neuromorphic systems can be applied to scientific data applications.
M. Girone, M. Elsing, L. Moneta, M. Pierini
Coffee will be served at 10h30