Neuromorphic Computing - From Robust Hardware Architectures to Testing Strategies
Abstract
This paper provides an overview of the challenges faced by hardware implemented Spiking Neural Networks, from device to circuit design, reliability and test. We present a comprehensive description of the state-of-the-art neuromorphic architectures inspired by brain computation, with special emphasis on Spiking Neural Networks (SNNs), together with emerging technologies that have enabled such systems, namely Phase Change and Metal Oxide Resistive Memories. Finally, we discuss the main challenges faced by hardware implementations of SNNs, their reliability and post-fabrication test issues.