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ORCIDICONS 2019: Knoxville, Tennessee, USA
- Thomas E. Potok, Catherine D. Schuman:
Proceedings of the International Conference on Neuromorphic Systems, ICONS 2019, Knoxville, Tennessee, USA, July 23-25, 2019. ACM 2019, ISBN 978-1-4503-7680-8 - Yandong Luo, Xiaochen Peng, Shimeng Yu
:
MLP+NeuroSimV3.0: Improving On-chip Learning Performance with Device to Algorithm Optimizations. 1:1-1:7 - Md. Shahanur Alam, B. Rasitha Fernando, Yassine Jaoudi, Chris Yakopcic, Raqibul Hasan, Tarek M. Taha, Guru Subramanyam:
Memristor Based Autoencoder for Unsupervised Real-Time Network Intrusion and Anomaly Detection. 2:1-2:8 - James B. Aimone, William Severa, Craig M. Vineyard:
Composing neural algorithms with Fugu. 3:1-3:8 - Susan M. Mniszewski:
Graph Partitioning as Quadratic Unconstrained Binary Optimization (QUBO) on Spiking Neuromorphic Hardware. 4:1-4:5 - Sandeep Madireddy, Angel Yanguas-Gil, Prasanna Balaprakash:
Neuromorphic Architecture Optimization for Task-Specific Dynamic Learning. 5:1-5:5 - Maximilian Liehr, Jubin Hazra, Karsten Beckmann, Wilkie Olin-Ammentorp, Nathaniel C. Cady, Ryan Weiss, Sagarvarma Sayyaparaju, Garrett S. Rose, Joseph Van Nostrand:
Fabrication and Performance of Hybrid ReRAM-CMOS Circuit Elements for Dynamic Neural Networks. 6:1-6:4 - Sumedha Gandharava Dahl, Robert C. Ivans, Kurtis D. Cantley:
Learning Behavior of Memristor-Based Neuromorphic Circuits in the Presence of Radiation. 7:1-7:7 - Kun Yue, Xiaoyu Wang, Jay Jadav, Akshay Vartak, Alice C. Parker:
Analog Neurons that Signal with Spiking Frequencies. 8:1-8:8 - Nathan Wycoff, Prasanna Balaprakash, Fangfang Xia:
Neuromorphic Acceleration for Approximate Bayesian Inference on Neural Networks via Permanent Dropout. 9:1-9:4 - Amar Shrestha, Haowen Fang, Qing Wu, Qinru Qiu:
Approximating Back-propagation for a Biologically Plausible Local Learning Rule in Spiking Neural Networks. 10:1-10:8 - Yijing Watkins, Austin Thresher, Peter F. Schultz, Andreas Wild, Andrew Sornborger, Garrett T. Kenyon:
Unsupervised Dictionary Learning via a Spiking Locally Competitive Algorithm. 11:1-11:5 - Edward Kim, Jessica Yarnall, Priya Shah, Garrett T. Kenyon:
A Neuromorphic Sparse Coding Defense to Adversarial Images. 12:1-12:8 - Craig M. Vineyard, Sam Green, William M. Severa, Çetin Kaya Koç:
Benchmarking Event-Driven Neuromorphic Architectures. 13:1-13:5 - Ruthvik Vaila, John N. Chiasson, Vishal Saxena:
Feature Extraction using Spiking Convolutional Neural Networks. 14:1-14:8 - Chenyuan Zhao, Lingjia Liu, Yang Yi:
Design and Analysis of Real Time Spiking Neural Network Decoder for Neuromorphic Chips. 15:1-15:4 - Younes Bouhadjar, Markus Diesmann, Rainer Waser, Dirk J. Wouters, Tom Tetzlaff:
Constraints on sequence processing speed in biological neuronal networks. 16:1-16:9 - Cory E. Merkel, Animesh Nikam:
A Low-Power Domino Logic Architecture for Memristor-Based Neuromorphic Computing. 17:1-17:4 - Wilkie Olin-Ammentorp, Nathaniel C. Cady:
Training Spiking Networks via Natural Evolution Strategies. 18:1-18:6 - Alexander Jones, Rashmi Jha, Ajey P. Jacob, Cory E. Merkel:
A Segmented Attractor Network for Neuromorphic Associative Learning. 19:1-19:8 - James B. Aimone, Ojas Parekh, Cynthia A. Phillips, Ali Pinar, William Severa, Helen Xu:
Dynamic Programming with Spiking Neural Computing. 20:1-20:9 - Aakanksha Mathuria, Dan W. Hammerstrom:
Approximate Pattern Matching using Hierarchical Graph Construction and Sparse Distributed Representation. 21:1-21:10
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