Prasanna Date Research Scientist Contact DATEPA@ORNL.GOV All Publications Adiabatic Quantum Support Vector Machines... 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 Neuromorphic Computing for Scientific Applications Hybrid Quantum-Classical Neural Networks Controller-Based Energy-Aware Wireless Sensor Network Routing Using Quantum Algorithms A Review of Non-Cognitive Applications for Neuromorphic Computing Neuromorphic Computing is Turing-Complete Semi-Supervised Graph Structure Learning on Neuromorphic Computers Pagination Current page 1 Page 2 Next page ›› Last page Last » Key Links Curriculum Vitae Google Scholar Web of Science ORCID LinkedIn GitHub Personal Webpage ResearchGate ORCiD Organizations Computing and Computational Sciences Directorate Computer Science and Mathematics Division Data and AI Systems Section Learning Systems Group
