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.

2020

  • Maryam Parsa, J. Parker Mitchell, Catherine D. Schuman, Robert M. Patton, Thomas E. Potok, and Kaushik Roy. “Bayesian Multi-Objective Hyperparameter Optimization for Accurate, Fast, and Efficient Neural Network Accelerator Design.” Frontiers of Neuroscience, 2020.
  • J. Parker Mitchell, Catherine D. Schuman, and Thomas E. Potok. “A Small, Low Cost Event-Driven Architecture for Spiking Neural Networks on FPGAs.” International Conference on Neuromorphic Systems (ICONS), 2020.
  • Daniel Elbrecht and Catherine D. Schuman. “Neuroevolution of Spiking Neural Networks Using Compositional Pattern Producing Networks.International Conference on Neuromorphic Systems (ICONS), 2020.
  • Kathleen Hamilton, Prasanna Date, Bill Kay and Catherine D. Schuman. “Modeling Epidemic Spread with Spike-Based Models.” International Conference on Neuromorphic Systems (ICONS), 2020.
  • Kathleen Hamilton, Tiffany Mintz, Prasanna Date, and Catherine D. Schuman. “Spike-Based Centrality Measures.” International Conference on Neuromorphic Systems (ICONS), 2020.
  • Catherine Schuman, J. Parker Mitchell, J. Travis Johnston, Maryam Parsa, Bill Kay, Prasanna Date and Robert Patton. “Resilience and Robustness of Spiking Neural Networks for Neuromorphic Systems.” International Joint Conference on Neural Networks (IJCNN) 2020, World Congress on Computational Intelligence (WCCI), 2020.
  • Maryam Parsa, Catherine D. Schuman, Prasanna Date, Derek C. Rose, Bill Kay, J. Parker Mitchell, Steven R. Young, Ryan Dellana, William Severa, Thomas E. Potok and Kaushik Roy. “Hyperparameter Optimization in Binary Communication Networks for Neuromorphic Deployment.” International Joint Conference on Neural Networks (IJCNN) 2020, World Congress on Computational Intelligence (WCCI), 2020.
  • Catherine Schuman, J. Parker Mitchell, Maryam Parsa, James Plank, Samuel Brown, Garrett Rose, Robert Patton and Thomas Potok. “Automated Design of Neuromorphic Networks for Scientific Applications at the Edge.” International Joint Conference on Neural Networks (IJCNN) 2020, World Congress on Computational Intelligence (WCCI), 2020.
  • Aaron Young, Adam Foshie, Mark Dean, James Plank, Garrett Rose, John Mitchell and Catherine Schuman. “Scaled-up Neuromorphic Array Communications Controller (SNACC) for Large-scale Neural Networks.” International Joint Conference on Neural Networks (IJCNN) 2020, World Congress on Computational Intelligence (WCCI), 2020.
  • Jonathan Ambrose, Adam Foshie, Mark Dean, James Plank, Garrett Rose, John Mitchell, Catherine Schuman and Grant Bruer. “GRANT: Ground-Roaming Autonomous Neuromorphic Targeter.” International Joint Conference on Neural Networks (IJCNN) 2020, World Congress on Computational Intelligence (WCCI), 2020.
  • Theodore Papamarkou, Hayley Guy, Bryce Kroencke, Jordan Miller, Preston Robinette, Daniel Schultz, Jacob Hinkle, Laura Pullum, Catherine Schuman, Jeremy Renshaw, Stylianos Chatzidakis. “Automated detection of corrosion in used nuclear fuel dry storage canisters using residual neural networks.” Nuclear Engineering and Technology, 2020.
  • Bill Kay, Prasanna Date, and Catherine D. Schuman. “Neuromorphic Graph Algorithms: Extracting Longest Shortest Paths and Minimum Spanning Trees.” Neuro-Inspired Computational Elements (NICE) Workshop, 2020.
  • J. Parker Mitchell, Catherine D. Schuman, Robert M. Patton, and Thomas E. Potok. “Caspian: A Neuromorphic Development Platform.” Neuro-Inspired Computational Elements (NICE) Workshop, 2020.
  • Catherine D. Schuman, J. Parker Mitchell, Robert M. Patton, Thomas E. Potok, and James S. Plank. “Evolutionary Optimization for Neuromorphic Systems.” Neuro-Inspired Computational Elements (NICE) Workshop, 2020.
  • Kathleen Hamilton, Catherine D. Schuman, Steven R. Young, Ryan S. Bennink, Neena Imam, and Travis S. Humble. “Accelerating scientific computing in the post-Moore’s era.” ACM Transactions on Parallel Computing (TOPC) 7.1 (2020): 1-31.

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