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Books and Theses
- 2020
[b1]Mark Plagge:
Extreme-scale neuromorphic architecture modeling using massively parallel optimistic simulation. Rensselaer Polytechnic Institute, USA, 2020
Journal Articles
- 2018
- [j1]Mark Plagge, Christopher D. Carothers, Elsa Gonsiorowski, Neil McGlohon
:
NeMo: A Massively Parallel Discrete-Event Simulation Model for Neuromorphic Architectures. ACM Trans. Model. Comput. Simul. 28(4): 30:1-30:25 (2018)
Conference and Workshop Papers
- 2024
- [c9]Mark Plagge, Suma George Cardwell, Frances S. Chance:
Expressive Dendrites in Spiking Networks. NICE 2024: 1-8 - 2023
- [c8]James A. Boyle, Mark Plagge, Suma George Cardwell, Frances S. Chance, Andreas Gerstlauer:
Performance and Energy Simulation of Spiking Neuromorphic Architectures for Fast Exploration. ICONS 2023: 19:1-19:4 - [c7]Michael Eydenberg, Mark Plagge, Siva Rajamanickam:
A Comparison of Spectral and Spatial Graph Convolutional Neural Network Kernels Using GraphSAGE-Sparse. IPDPS Workshops 2023: 189-198 - 2022
- [c6]Mark Plagge, Ben Feinberg, John McFarland, Fred Rothganger, Sapan Agarwal, Amro Awad, Clayton Hughes, Suma George Cardwell:
ATHENA: Enabling Codesign for Next-Generation AI/ML Architectures. ICRC 2022: 13-23 - 2020
- [c5]Craig M. Vineyard, Mark Plagge, Sam Green:
Comparing Neural Accelerators & Neuromorphic Architectures The False Idol of Operations. NICE 2020: 3:1-3:6 - 2019
- [c4]Caitlin J. Ross, Noah Wolfe, Mark Plagge, Christopher D. Carothers, Misbah Mubarak, Robert B. Ross:
Using Scientific Visualization Techniques to Visualize Parallel Network Simulations. SIGSIM-PADS 2019: 197-200 - 2018
- [c3]Noah Wolfe, Mark Plagge, Christopher D. Carothers, Misbah Mubarak, Robert B. Ross:
Evaluating the Impact of Spiking Neural Network Traffic on Extreme-Scale Hybrid Systems. PMBS@SC 2018: 108-120 - 2016
- [c2]Mark Plagge, Christopher D. Carothers, Elsa Gonsiorowski:
NeMo: A Massively Parallel Discrete-Event Simulation Model for Neuromorphic Architectures. SIGSIM-PADS 2016: 233-244 - 2013
- [c1]Mark Plagge:
Using artificial neural networks to predict first-year traditional students second year retention rates. ACM Southeast Regional Conference 2013: 17:1-17:5
Coauthor Index
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