Publications

Neuromorphic Computing Survey Paper

Title:  A Survey of Neuromorphic Computing and Neural Networks in Hardware

Abstract: Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices, and models that contrast the pervasive von Neumann computer architecture. This biologically inspired approach has created highly connected synthetic neurons and synapses that can be used to model neuroscience theories as well as solve challenging machine learning problems. The promise of the technology is to create a brain-like ability to learn and adapt, but the technical challenges are significant, starting with an accurate neuroscience model of how the brain works, to finding materials and engineering breakthroughs to build devices to support these models, to creating a programming framework so the systems can learn, to creating applications with brain-like capabilities. In this work, we provide a comprehensive survey of the research and motivations for neuromorphic computing over its history. We begin with a 35-year review of the motivations and drivers of neuromorphic computing, then look at the major research areas of the field, which we define as neuro-inspired models, algorithms and learning approaches, hardware and devices, supporting systems, and finally applications. We conclude with a broad discussion on the major research topics that need to be addressed in the coming years to see the promise of neuromorphic computing fulfilled. The goals of this work are to provide an exhaustive review of the research conducted in neuromorphic computing since the inception of the term, and to motivate further work by illuminating gaps in the field where new research is needed.

2017

  • Catherine D. Schuman, Raphael Pooser, Tiffany Mintz, Md Musabbir Adnan, Garrett S. Rose, Bon Woong Ku, and Sung Kyu Lim. “Simulating and Estimating the Behavior of a Neuromorphic Co-Processor.” International Workshop on Post-Moore’s Era Supercomputing (PMES), November 2017. Accepted.
  • James S. Plank, Garrett S. Rose, Mark E. Dean, Catherine D. Schuman, and Natheniel C. Cady. “A Unified Hardware/Software Co-Design Framework for Neuromorphic Computing Devices and Applications.” IEEE International Conference on Rebooting Computing (ICRC 2017), November 2017. Accepted.
  • J. Parker Mitchell, Grant Bruer, Mark E. Dean, James S. Plank, Garrett S. Rose, and Catherine D. Schuman. “NeoN: Neuromorphic Control for Autonomous Robotic Navigation.” 2017 IEEE 5th International Symposium on Robotics and Intelligent Sensors, October 2017. Accepted.
  • Catherine D. Schuman, James S. Plank, Garrett S. Rose, Gangotree Chakma, Austin Wyer, Grant Bruer, and Nouamane Laanait. “A Programming Framework for Neuromorphic Systems with Emerging Technologies.” 4th ACM International Conference on Nanoscale Computing and Communication, September 2017. Accepted.
  • Catherine D. Schuman. “The Effect of Biologically-Inspired Mechanisms in Spiking Neural Networks for Neuromorphic Implementation.” International Joint Conference on Neural Networks 2017, May 2017.
  • Aleksander Klibisz, Grant Bruer, James S. Plank, and Catherine D. Schuman. “Structure-based Fitness Prediction for the Variable-structure DANNA Neuromorphic Architecture.”  International Joint Conference on Neural Networks 2017, May 2017.
  • Plank, James S., Garrett S. Rose, Mark E. Dean and Catherine D. Schuman. “A CAD System for Exploring Neuromorphic Computing with Emerging Technologies.” 42nd Annual GOMACTech Conference. March 2017.

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