Neuromorphic technologies for next-generation cognitive computing

Abstract

We describe IBM's roadmap for Neuromorphic Technologies to drive next-generation cognitive computing, ranging from nanodevice-based hardware for accelerating well-known supervised-learning algorithms (which happen to rely on static, labeled data), to emerging, biologically-inspired algorithms capable of learning from temporal, unlabeled data. The various hardware-centric neuromorphic projects currently underway at IBM Research will be surveyed, with a focus on the use of Non-Volatile Memory (NVM) for on-chip acceleration of the training of Deep Neural Networks (DNNs).