Tutorial: Large-Scale Spiking Neuromorphic Architecture Exploration using SANA-FE

James A. Boyle, Mark Plagge, Suma George Cardwell, Frances S. Chance, Andreas Gerstlauer

Abstract: Neuromorphic computing uses brain-inspired concepts to accelerate and efficiently execute a wide range of applications, such as mimicking biological circuits, solving NPhard optimization problems and accelerating machine learning at the edge. In particular, neuromorphic architectures to efficiently execute Spiking Neural Networks (SNNs) have gained popularity. SNNs extend artificial neural networks (ANNs) by encoding information in time as either rates or delays between spiking events, shared between neurons via their weighted connections. SNN-based platforms are event-driven, resulting in naturally sparse, noise-tolerant and power-efficient computation. In this tutorial, we present the state-of-the-art in scalable digital and analog spiking neuromorphic system architectures, and discuss current research trends within the neuromorphic architecture field at the system level. We further introduce our SANA-FE tool for Simulation of Advanced Neuromorphic Architectures for Fast Exploration, which has been developed as part of a collaboration between the University of Texas at Austin and Sandia National Laboratories. SANA-FE allows for modeling and performance-power prediction of different spiking hardware architectures executing SNN applications to support rapid, early system-level design-space exploration, hardware-aware application development and system architecture co-design. The tutorial includes a hands-on component in which SANA-FE’s capabilities are demonstrated and used to perform system design and application mapping case studies.