Large-scale neuromorphic computing systems.

Invited Talk: Neuromorphic Computing Principles, Achievements, and Potentials

Neural networks have recently taken the field of machine learning by storm. Their success rests upon the availability of high performance computing hardware which allows to train very wide and deep networks. Traditional neural networks have very limited biological realism. Recent work on more brain-like hardware architectures has led to first large-scale implementations of neuromorphic computin...

Neuromorphic Sensory Processing Using Analog Very Large - Scale Integration Circuits

Neuromorphic Sensory Processing Using Analog Very Large-Scale Integration Circuits Neuromorphic circuits are analog circuits that implement models of biological systems for sensory processing. Because the neuromorphic circuits share the same physical constraints as their biological counterparts, they have similar organizational structures and use similar strategies for optimizing robustness to ...

Neuromorphic Logic Networks and Robust Stochastic Computing Under Large Perturbations and Uncertainties

This paper studies robust fault-tolerant neuromorphic computing to support enabling applicationspecific design and enable emerging nanoscale microelectronics. We develop an energy-centric probabilistic design concept and propose a library of neuromorphic networks for logic functions. These developments enable robust failure tolerance, adaptation and reconfiguration of complex large-scale networ...

Neuromorphic VLSI selective attention systems: from single chip solutions to multi-chip systems

Guest Editorial: Design and Applications of Neuromorphic Computing System

AS artificial intelligence technology becomes pervasive in society and ubiquitous in our lives, the desire for embedded-everywhere and human-centric computational intelligence systems calls for an intelligent computation paradigm. However, the applications of machine learning and neural networks involve large, noisy, incomplete, natural data sets that do not lend themselves to convenient soluti...