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.

2022

Bon Woong Ku, Catherine D. Schuman, Md Musabbir Adnan, Tiffany M. Mintz, Raphael Pooser, Kathleen E. Hamilton, Garrett S. Rose, and Sung Kyu Lim. “Unsupervised Digit Recognition Using Cosine Similarity In A Neuromemristive Competitive Learning System.” ACM Journal on Emerging Technologies in Computing Systems (JETC) 18, no. 2 (2022): 1-20.
Bryan P. Maldonado, Brian C. Kaul, Catherine D. Schuman, Steven R. Young, and J. Parker Mitchell. “Next-Cycle Optimal Dilute Combustion Control via Online Learning of Cycle-to-Cycle Variability Using Kernel Density Estimators.” IEEE Transactions on Control Systems Technology (2022).
Catherine Schuman, Robert Patton, Shruti Kulkarni, Maryam Parsa, Christopher Stahl, N. Quentin Haas, J. Parker Mitchell, Shay Snyder, Amelie Nagle, Alexandra Shanafield and Thomas Potok. “Evolutionary vs imitation learning for neuromorphic control at the edge.” Neuromorphic Computing and Engineering 2, no. 1 (2022): 014002.
Catherine D. Schuman, Shruti R. Kulkarni, Maryam Parsa, J. Parker Mitchell, and Bill Kay. “Opportunities for neuromorphic computing algorithms and applications.” Nature Computational Science 2, no. 1 (2022): 10-19.

2021

Catherine D. Schuman, Steven R. Young, Bryan P. Maldonado, and Brian C. Kaul. “Real-Time Evolution and Deployment of Neuromorphic Computing at The Edge.” In 2021 12th International Green and Sustainable Computing Conference (IGSC), pp. 1-8. IEEE, 2021.
Suman Debnath, Shruti Kulkarni, and Catherine Schuman. “Intelligent Prediction of States in Multi-port Autonomous Reconfigurable Solar power plant (MARS).” In 2021 IEEE Energy Conversion Congress and Exposition (ECCE), pp. 1339-1346. IEEE, 2021.
Eric O. Scott, Mark Coletti, Catherine D. Schuman, Bill Kay, Shruti R. Kulkarni, Maryam Parsa, and Kenneth A. De Jong. “Avoiding excess computation in asynchronous evolutionary algorithms.” In UK Workshop on Computational Intelligence, pp. 71-82. Springer, Cham, 2021.
Shruti R. Kulkarni, Maryam Parsa, J. Parker Mitchell, and Catherine D. Schuman. “Benchmarking the performance of neuromorphic and spiking neural network simulators.” Neurocomputing 447 (2021): 145-160.
J. Parker Mitchell, and Catherine Schuman. “Low Power Hardware-In-The-Loop Neuromorphic Training Accelerator.” In International Conference on Neuromorphic Systems 2021, pp. 1-4. 2021.
Robert Patton, Catherine Schuman, Shruti Kulkarni, Maryam Parsa, J. Parker Mitchell, N. Quentin Haas, Christopher Stahl et al. “Neuromorphic computing for autonomous racing.” In International Conference on Neuromorphic Systems 2021, pp. 1-5. 2021.
Shruti Kulkarni, Maryam Parsa, J. Parker Mitchell, and Catherine Schuman. “Training Spiking Neural Networks with Synaptic Plasticity under Integer Representation.” In International Conference on Neuromorphic Systems 2021, pp. 1-7. 2021.
Bill Kay, Catherine Schuman, Jade O’Connor, Prasanna Date, and Thomas Potok. “Neuromorphic Graph Algorithms: Cycle Detection, Odd Cycle Detection, and Max Flow.” In International Conference on Neuromorphic Systems 2021, pp. 1-7. 2021.
Prasanna Date, Bill Kay, Catherine Schuman, Robert Patton, and Thomas Potok. “Computational Complexity of Neuromorphic Algorithms.” In International Conference on Neuromorphic Systems 2021, pp. 1-7. 2021.
James Plank, Chaohui Zheng, Catherine Schuman, and Christopher Dean. “Spiking Neuromorphic Networks for Binary Tasks.” In International Conference on Neuromorphic Systems 2021, pp. 1-9. 2021.
Maryam Parsa, Catherine Schuman, Nitin Rathi, Amir Ziabari, Derek Rose, J. Parker Mitchell, J. Travis Johnston, Bill Kay, Steven Young, and Kaushik Roy. “Accurate and Accelerated Neuromorphic Network Design Leveraging A Bayesian Hyperparameter Pareto Optimization Approach.” In International Conference on Neuromorphic Systems 2021, pp. 1-8. 2021.
Catherine D. Schuman, James S. Plank, Maryam Parsa, Shruti R. Kulkarni, Nicholas Skuda, and J. Parker Mitchell. “A software framework for comparing training approaches for spiking neuromorphic systems.” In 2021 International Joint Conference on Neural Networks (IJCNN), pp. 1-10. IEEE, 2021.
Md Musabbir Adnan, Sagarvarma Sayyaparaju, Samuel D. Brown, Mst Shamim Ara Shawkat, Catherine D. Schuman, and Garrett S. Rose. “Design of a Robust Memristive Spiking Neuromorphic System with Unsupervised Learning in Hardware.” ACM Journal on Emerging Technologies in Computing Systems (JETC) 17, no. 4 (2021): 1-26.
Maryam Parsa, Shruti R. Kulkarni, Mark Coletti, Jeffrey Bassett, J. Parker Mitchell, and Catherine D. Schuman. “Multi-Objective Hyperparameter Optimization for Spiking Neural Network Neuroevolution.” In 2021 IEEE Congress on Evolutionary Computation (CEC), pp. 1225-1232. IEEE, 2021.
Catherine D. Schuman, Bill Kay, Prasanna Date, Ramakrishnan Kannan, Piyush Sao, and Thomas E. Potok. “Sparse Binary Matrix-Vector Multiplication on Neuromorphic Computers.” In 2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp. 308-311. IEEE, 2021.
Bryan P. Maldonado, Brian C. Kaul, Catherine D. Schuman, Steven R. Young, and J. Parker Mitchell. Dilute combustion control using spiking neural networks. No. 2021-01-0534. SAE Technical Paper, 2021.
Theodore Papamarkou, Hayley Guy, Bryce Kroencke, Jordan Miller, Preston Robinette, Daniel
Schultz, Jacob Hinkle, Laura Pullum, Catherine Schuman, Jeremy Renshaw, Stylianos Chatzi-
dakis. “Automated detection of corrosion in used nuclear fuel dry storage canisters using residual
neural networks.” Nuclear Engineering and Technology 53, no. 2 (2021): 657-665.
Wilkie Olin-Ammentorp, Karsten Beckmann, Catherine D. Schuman, James S. Plank, and Nathaniel C. Cady. “Stochasticity and robustness in spiking neural networks.” Neurocomputing 419 (2021): 23-36.

2020

Bryan P. Maldonado, Brian C. Kaul, Catherine D. Schuman, Steven R. Young, and J. Parker Mitchell. “Next-cycle optimal fuel control for cycle-to-cycle variability reduction in egr-diluted combustion.” IEEE Control Systems Letters 5, no. 6 (2020): 2204-2209.
Daniel Elbrecht, Shruti R. Kulkarni, Maryam Parsa, J. Parker Mitchell, and Catherine D. Schuman. “Evolving ensembles of spiking neural networks for neuromorphic systems.” In 2020 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1989-1994. IEEE, 2020.
Daniel Elbrecht, Maryam Parsa, Shruti R. Kulkarni, J. Parker Mitchell, and Catherine D. Schuman. “Training Spiking Neural Networks Using Combined Learning Approaches.” In 2020 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1995-2001. IEEE, 2020.
Catherine D. Schuman, Steven R. Young, J. Parker Mitchell, J. Travis Johnston, Derek Rose, Bryan P. Maldonado, and Brian C. Kaul. “Low size, weight, and power neuromorphic computing to improve combustion engine efficiency.” In 2020 11th International Green and Sustainable Computing Workshops (IGSC), pp. 1-8. IEEE, 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.

2019

Junghoon Chae, Catherine D. Schuman, Steven R. Young, J. Travis Johnston, Derek C. Rose, Robert M. Patton, and Thomas E. Potok. “Visualization System for Evolutionary Neural Networks for Deep Learning.” First International Workshop on Big Data Tools, Methods, and Use Cases for Innovative Scientific Discovery (BTSD) 2019, IEEE Big Data, 2019.
Maryam Parsa, J. Parker Mitchell, Catherine D. Schuman, Robert M. Patton, Thomas E. Potok, and Kaushik Roy. “Bayesian-based Hyperparameter Optimization for Spiking Neuromorphic Systems.” 2nd Workshop on Energy-Efficient Machine Learning and Big Data Analytics, IEEE Big Data, 2019.
Steven R. Young, J. Travis Johnston, Catherine D. Schuman, Pravallika Devineni, Bill Kay, Derek C. Rose, Maryam Parsa, Robert M. Patton, and Thomas E. Potok. “Evolving Energy Efficient Convolutional Neural Networks.” 2nd Workshop on Energy-Efficient Machine Learning and Big Data Analytics, IEEE Big Data, 2019.
Robert M. Patton, J. Travis Johnston, Steven R. Young, Catherine D. Schuman, Thomas E. Potok, Derek C. Rose, Seung-Hwan Lim, Junghoon Chae, Le Hou, Shahira Abousamra, Dimitris Samaras, and Joel Saltz. “Exascale Deep Learning to Accelerate Cancer Research.” IEEE Big Data, IEEE, 2019.
J. Travis Johnston, Steven R. Young, Catherine D. Schuman, Junghoon Chae, Don D. March, Robert M. Patton, and Thomas E. Potok. “Fine-grained Exploitation of Mixed Precision for Faster CNN Training.” Machine Learning in High Performance Computing Environments Workshop, Supercomputing, 2019.
Mihaela Dimovska, J. Travis Johnston, Catherine D. Schuman, J. Parker Mitchell, and Thomas E. Potok. “Multi-Objective Optimization for Size and Resilience of Spiking Neural Networks.” IEEE Ubiquitous Computing, Electronics and Mobile Communication Conference. IEEE, 2019.
Catherine D. Schuman, James S. Plank, Grant Bruer, and Jeremy Anatharaj. “Non-Traditional Input Encoding Schemes for Spiking Neuromorphic Systems.” International Joint Conference on Neural Networks 2019. IEEE, 2019.
John J.M. Reynolds, James S. Plank, Catherine D. Schuman. “Intelligent Reservoir Generation for Liquid State Machines using Evolutionary Optimization.” International Joint Conference on Neural Networks 2019. IEEE, 2019.
Catherine D. Schuman, James S. Plank, Robert M. Patton, and Thomas E. Potok. “Island model for parallel evolutionary optimization of spiking neuromorphic computing.” Proceedings of the Genetic and Evolutionary Computation Conference Companion. ACM, 2019.
Catherine D. Schuman, Kathleen Hamilton, Tiffany Mintz, Md MusabbirAdnan, Bon Woong Ku, Sung-Kyu Lim, and Garrett S. Rose. “Shortest Path and Neighborhood Subgraph Extraction on a Spiking Memristive Neuromorphic Implementation.” Neuro-Inspired Computational Elements Workshop (NICE ’19), ACM, 2019.
James S. Plank, Charles Rizzo, Kirolos Shahat, Grant Bruer, Trevor Dixon, Michael Goin, Grace Zhao, Jeremy Anantharaj, Catherine D. Schuman, Mark E. Dean, Garrett S. Rose, Nathaniel C. Cady and Joseph Van Nostrand. “The TENNLab Suite of LIDAR-Based Control Applications for Recurrent, Spiking, Neuromorphic Systems.” 44th Annual GOMACTech Conference, 2019.
Linghao Song, Fan Chen, Steven R. Young, Catherine D. Schuman, Gabriel Perdue and Thomas E. Potok. “Deep Learning for Vertex Reconstruction of Neutrino-Nucleus Interaction Events with Combined Energy and Time Data.” ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019.
Prasanna Date, Robert Patton, Catherine D. Schuman, and Thomas E. Potok. “Efficiently Embedding QUBO Problems on Adiabatic Quantum Computers.” Quantum Information Processing: 18 (4), Springer, 2019.
Prasanna Date, Catherine D. Schuman, Robert Patton, and Thomas E. Potok. “A Classical- Quantum Hybrid Approach for Unsupervised Probabilistic Machine Learning.” Future of Information and Communication Conference. Springer, 2019.

2018

James S. Plank, Catherine D. Schuman, Grant Bruer, Mark E. Dean, and Garrett S. Rose. “The TENNLab Exploratory Neuromorphic Computing Framework.” IEEE Letters of the Computer Society, IEEE, December 2018.
Robert M. Patton, J. Travis Johnston, Steven R. Young, Catherine D. Schuman, Don D. March, Thomas E. Potok, Derek C. Rose, Seung-Hwan Lim, Thomas P. Karnowski, Maxim A. Ziatdinov, Sergei V. Kalinin. “167-PFlops deep learning for electron microscopy: from learning physics to atomic manipulation.” Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis, IEEE, November 2018. ACM Gordon Bell Prize Finalist.
Sonia Buckley, Adam N. McCaughan, Jeff Chiles, Richard P. Mirin, Sae Woo Nam, Jeffrey M. Shainline, Grant Bruer, James S. Plank, and Catherine D. Schuman. “Design of superconducting optoelectronic networks for neuromorphic computing.” 2018 IEEE International Conference on Rebooting Computing (ICRC), IEEE, 2018.
Md Sakib Hasan, Catherine D. Schuman, Joseph S. Najem, Ryan Weiss, Nicholas D. Skuda, Alex Belianinov, C. Patrick Collier, Stephen A. Sarles, and Garrett S. Rose. “Biomimetic, Soft- Material Synapse for Neuromorphic Computing: from Device to Network.” 2018 IEEE 13th Dallas Circuits and Systems Conference (DCAS), IEEE, 2018.
Md Musabbir Adnan, Sagarvarma Sayyaparaju, Garrett S. Rose, Catherine D. Schuman, Bon Woong Ku, and Sung-Kyu Lim. “A Twin Memristor Synapse for Spike Timing Dependent Learning in Neuromorphic Systems.” 2018 31st IEEE International System-on-Chip Conference (SOCC) , IEEE, 2018.
Md Sakib Hasan, Joseph S. Najem, Ryan Weiss, Catherine D. Schuman, Alex Belianinov, C. Patrick Collier, Stephen A. Sarles, and Garrett S. Rose. “Response of a Memristive Biomembrane and Demonstration of Potential Use in Online Learning.” Proceedings of 2018 IEEE 13th Nanotechnology Materials and Devices Conference (NMDC), IEEE, 2018.
Ryan Weiss, Joseph S. Najem, Md Sakib Hasan, Catherine D. Schuman, Alex Belianinov, C. Patrick Collier, Stephen A. Sarles, and Garrett S. Rose. “A Soft-Matter Biomolecular Memristor Synapse for Neuromorphic Systems.” Biomedical Circuits and Systems Conference, IEEE, October 2018.
Catherine D. Schuman, Grant Bruer, Aaron Young, Mark Dean, and James S. Plank. “Understanding Selection And Diversity For Evolution Of Spiking Recurrent Neural Networks.” International Joint Conference on Neural Networks (IJCNN), IEEE, July 2018.
Aaron Young, Mark Dean, James S. Plank, Garrett S. Rose, and Catherine D. Schuman. “Neuromorphic Array Communications Controller to Support Large-Scale Neural Networks.” International Joint Conference on Neural Networks (IJCNN), IEEE, July 2018.
Thomas E. Potok, Catherine D. Schuman, Steven Young, Robert Patton, Federico Spedalieri, Jeremy Liu, Ke-Thia Yao, Garrett Rose, and Gangotree Chakma. “A Study of Complex Deep Learning Networks on High-Performance, Neuromorphic, and Quantum Computers.” ACM Journal on Emerging Technologies in Computing Systems (JETC). 14, no. 2, ACM, 2018.
Kathleen Hamilton and Catherine D. Schuman. “Towards adaptive spiking label propagation.” Proceedings of the International Conference on Neuromorphic Systems. ACM, July 2018.
John Reynolds, James S. Plank, Catherine D. Schuman, Grant Bruer, Adam Disney, Mark E. Dean, and Garrett S. Rose. “A Comparison of Neuromorphic Classification Tasks.” Proceedings of the International Conference on Neuromorphic Systems. ACM, July 2018.
Kathleen Hamilton, Catherine D. Schuman, Steven R. Young, Neena Imam, and Travis Humble. “Neural Networks and Graph Algorithms with Next-Generation Processors.” 2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). IEEE, May 2018.
Gangotree Chakma, Nicholas D. Skuda, Catherine D. Schuman}, James S. Plank, Mark E. Dean, and Garrett S. Rose. “Energy and Area Efficiency in Neuromorphic Computing for Resource Constrained Devices.” 28th ACM Great Lakes Symposium on VLSI (GLSVLSI). ACM, May 2018.
Jeremy Liu, Federico Spedalieri, Ke-Thia Yao, Thomas E. Potok, Catherine D. Schuman, Steven R. Young, Robert Patton, Garrett S. Rose, and Gangotree Chakma. “Adiabatic Quantum Computation Applied to Deep Learning Networks.” Entropy. MDPI, May 2018.
Nicholas D. Skuda, Catherine D. Schuman, Gangotree Chakma, James S. Plank, and Garrett S. Rose. “High-Level Simulation for Spiking Neuromorphic Computing Systems.” 2018 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, May 2018.
Catherine D. Schuman, Thomas E. Potok, Steven Young, Robert Patton, Gabriel Perdue, Gangotree Chakma, Austin Wyer, Garrett S. Rose. “Neuromorphic computing for temporal scientific data classification.” NCS ’17: Neuromorphic Computing Symposium, April 2018.
Austin Wyer, Md Musabbir Adnan, Bon Woong Ku, Sung Kyu Lim, Catherine D. Schuman, Raphael C. Pooser, Garrett S. Rose. “Evaluating online-learning in memristive neuromorphic circuits.” NCS ’17: Neuromorphic Computing Symposium, April 2018.
Joseph S. Najem, Graham J. Taylor, Ryan J. Weiss, Md Sakib Hasan, Garrett Rose, Catherine D. Schuman, Alex Belianinov, C. Patrick Collier, and Stephen A. Sarles. “Memristive Ion Channel-Doped Biomembranes as Synaptic Mimics.” ACS nano, March 2018.

2017

Gangotree Chakma, Md Musabbir Adnan, Austin R. Wyer, Ryan Weiss, Catherine D. Schuman, and Garrett S. Rose. “Memristive Mixed-Signal Neuromorphic Systems: Energy-Efficient Learning at the Circuit-Level.” IEEE Journal on Emerging and Selected Topics in Circuits and Systems, November 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.
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.
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.
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.

2016

Potok, Thomas E., Catherine D. Schuman, Steven R. Young, Robert M. Patton, Federico Spedalieri, Jeremy Liu, Ke-Thia Yao, Garrett Rose, and Gangotree Chakma. “A study of complex deep learning networks on high performance, neuromorphic, and quantum computers.” In Proceedings of the Workshop on Machine Learning in High Performance Computing Environments, pp. 47-55. IEEE Press, 2016.
Schuman, Catherine D., Adam Disney, Susheela P. Singh, Grant Bruer, J. Parker Mitchell, Aleksander Klibisz, and James S. Plank. “Parallel evolutionary optimization for neuromorphic network training.” In Proceedings of the Workshop on Machine Learning in High Performance Computing Environments, pp. 36-46. IEEE Press, 2016.
Schuman, Catherine D., J. Douglas Birdwell, Mark E. Dean, James S. Plank, and Garrett S. Rose. “Neuromorphic Computing: A Post-Moore’s Law Complementary Architecture.” International Workshop on Post-Moore’s Era Supercomputing (PMES). November 2016.
Disney, Adam, John Reynolds, Catherine D. Schuman, Aleksander Klibisz, Aaron Young, and James S. Plank. “DANNA: A neuromorphic software ecosystem.” Biologically Inspired Cognitive Architectures 17 (2016): 49-56.
Dean, Mark E., Jason Chan, Christopher Daffron, Adam Disney, John Reynolds, Garrett Rose, James S. Plank, J. Douglas Birdwell, and Catherine D. Schuman. “An Application Development Platform for Neuromorphic Computing.” In Neural Networks (IJCNN), 2016 International Joint Conference on, pp. 1347-1354. IEEE, 2016.
Schuman, Catherine D., James S. Plank, Adam Disney, and John Reynolds. “An evolutionary optimization framework for neural networks and neuromorphic architectures.” In Neural Networks (IJCNN), 2016 International Joint Conference on, pp. 145-154. IEEE, 2016.
Daffron, Christopher, Jason Chan, Adam Disney, Luke Bechtel, Ryan Wagner, Mark E. Dean, Garrett S. Rose, James S. Plank, J. Douglas Birdwell, and Catherine D. Schuman. “Extensions and enhancements for the DANNA neuromorphic architecture.” In SoutheastCon, 2016, pp. 1-4. IEEE, 2016.

2015

Schuman, Catherine D. “Neuroscience-Inspired Dynamic Architectures.” Dissertation, University of Tennessee.
Schuman, Catherine D., Adam Disney, and John Reynolds. “Dynamic adaptive neural network arrays: a neuromorphic architecture.” In Proceedings of the Workshop on Machine Learning in High-Performance Computing Environments, p. 3. ACM, 2015.

2014

Drouhard, Margaret, Catherine D. Schuman, J. Douglas Birdwell, and Mark E. Dean. “Visual analytics for neuroscience-inspired dynamic architectures.” In Foundations of Computational Intelligence (FOCI), 2014 IEEE Symposium on, pp. 106-113. IEEE, 2014.
Dean, Mark E., Catherine D. Schuman, and J. Douglas Birdwell. “Dynamic adaptive neural network array.” In International Conference on Unconventional Computation and Natural Computation, pp. 129-141. Springer International Publishing, 2014.
Schuman, Catherine D., J. Douglas Birdwell, and Mark Dean. “Neuroscience-inspired dynamic architectures.” In Biomedical Science and Engineering Center Conference (BSEC), 2014 Annual Oak Ridge National Laboratory, pp. 1-4. IEEE, 2014.
Schuman, Catherine D., J. Douglas Birdwell, and Mark E. Dean. “Spatiotemporal classification using neuroscience-inspired dynamic architectures.” Procedia Computer Science 41 (2014): 89-97.

2013

Schuman, Catherine D., and J. Douglas Birdwell. “Dynamic artificial neural networks with affective systems.” PloS one 8, no. 11 (2013): e80455.
Schuman, Catherine D., and J. Douglas Birdwell. “Variable structure dynamic artificial neural networks.” Biologically Inspired Cognitive Architectures 6 (2013): 126-130.

2012

Plank, James S., Catherine D. Schuman, and B. Devin Robison. “Heuristics for optimizing matrix-based erasure codes for fault-tolerant storage systems.” In Dependable Systems and Networks (DSN), 2012 42nd Annual IEEE/IFIP International Conference on, pp. 1-12. IEEE, 2012.

2009

Plank, James S., Jianqiang Luo, Catherine D. Schuman, Lihao Xu, and Zooko Wilcox-O’Hearn. “A Performance Evaluation and Examination of Open-Source Erasure Coding Libraries for Storage.” In Fast, vol. 9, pp. 253-265. 2009.