Prasanna Date Research Scientist Contact DATEPA@ORNL.GOV All Publications Resilience and Robustness of Spiking Neural Networks for Neuromorphic Systems Hyperparameter Optimization in Binary Communication Networks for Neuromorphic Deployment Spike-based graph centrality measures Modeling epidemic spread with spike-based models Neuromorphic Graph Algorithms: Extracting Longest Shortest Paths and Minimum Spanning Trees Defining quantum-ready primitives for hybrid HPC-QC supercomputing: a case study in Hamiltonian simulation iFair: Achieving Fairness in the Allocation of Scarce Resources for Senior Health Care Quantum discriminator for binary classification Arithmetic Primitives for Efficient Neuromorphic Computing An FPGA-Based Neuromorphic Processor with All-to-All Connectivity Hyperparameter Optimization and Feature Inclusion in Graph Neural Networks for Spiking Implementation A Novel Spatial-Temporal Variational Quantum Circuit to Enable Deep Learning on NISQ Devices Characterizing Quantum Classifier Utility in Natural Language Processing Workflows On-Sensor Data Filtering using Neuromorphic Computing for High Energy Physics Experiments SuperNeuro: A Fast and Scalable Simulator for Neuromorphic Computing Encoding integers and rationals on neuromorphic computers using virtual neuron Abisko: Deep codesign of an architecture for spiking neural networks using novel neuromorphic materials Virtual Neuron: A Neuromorphic Approach for Encoding Numbers Hybrid Quantum-Classical Neural Networks Neuromorphic Computing for Scientific Applications Controller-Based Energy-Aware Wireless Sensor Network Routing Using Quantum Algorithms A Review of Non-Cognitive Applications for Neuromorphic Computing Semi-Supervised Graph Structure Learning on Neuromorphic Computers Neuromorphic Computing is Turing-Complete Quantum Computing Systems and Software for High-energy Physics Research Pagination Current page 1 Page 2 Next page ›â¶Äº Last page Last » Key Links Curriculum Vitae Organizations Computing and Computational Sciences Directorate Computer Science and Mathematics Division Data and AI Systems Section Learning Systems Group
