142 citations · 284 across the 19 of their papers we have counts for
20 papers
SuperNeuroMAT: An Efficient Matrix-based Simulator for Spiking Neural Networks
Prasanna Date, Kevin Zhu, Shruti Kulkarni +12
Spiking neural networks (SNNs) offer a promising pathway to energy-efficient AI and brain-inspired computing. However, their widespread adoption is hindered by a lack of fast, acce…
Neuromorphic Processor Employing FPGA Technology with Universal Interconnections
Pracheta Harlikar, Abdel-Hameed A. Badawy, Prasanna Date
Neuromorphic computing, inspired by biological neural systems, holds immense promise for ultra-low-power and real-time inference applications. However, limited access to flexible,…
NeuroCoreX: An Open-Source FPGA-Based Spiking Neural Network Emulator with On-Chip Learning
Ashish Gautam, Prasanna Date, Shruti Kulkarni +2
Spiking Neural Networks (SNNs) are computational models inspired by the structure and dynamics of biological neuronal networks. Their event-driven nature enables them to achieve hi…
On-Sensor Data Filtering using Neuromorphic Computing for High Energy Physics Experiments
Shruti R. Kulkarni, Aaron Young, Prasanna Date +12
This work describes the investigation of neuromorphic computing-based spiking neural network (SNN) models used to filter data from sensor electronics in high energy physics experim…
A Novel Spatial-Temporal Variational Quantum Circuit to Enable Deep Learning on NISQ Devices
Jinyang Li, Zhepeng Wang, Zhirui Hu +3
Quantum computing presents a promising approach for machine learning with its capability for extremely parallel computation in high-dimension through superposition and entanglement…
SuperNeuro: A Fast and Scalable Simulator for Neuromorphic Computing
Prasanna Date, Chathika Gunaratne, Shruti Kulkarni +3
In many neuromorphic workflows, simulators play a vital role for important tasks such as training spiking neural networks (SNNs), running neuroscience simulations, and designing, i…
Encoding Integers and Rationals on Neuromorphic Computers using Virtual Neuron
Prasanna Date, Shruti Kulkarni, Aaron Young +3
Neuromorphic computers perform computations by emulating the human brain, and use extremely low power. They are expected to be indispensable for energy-efficient computing in the f…
Snowmass White Paper: Quantum Computing Systems and Software for High-energy Physics Research
Travis S. Humble, Andrea Delgado, Raphael Pooser +23
Quantum computing offers a new paradigm for advancing high-energy physics research by enabling novel methods for representing and reasoning about fundamental quantum mechanical phe…
Quantum computing for data analysis in high energy physics
Andrea Delgado, Kathleen E. Hamilton, Prasanna Date +22
Some of the biggest achievements of the modern era of particle physics, such as the discovery of the Higgs boson, have been made possible by the tremendous effort in building and o…
A Hybrid Quantum-Classical Neural Network Architecture for Binary Classification
Davis Arthur, Prasanna Date
Deep learning is one of the most successful and far-reaching strategies used in machine learning today. However, the scale and utility of neural networks is still greatly limited b…
Towards a comprehensive visualization of structure in data
Joan Garriga, Frederic Bartumeus
Dimensional data reduction methods are fundamental to explore and visualize large data sets. Basic requirements for unsupervised data exploration are simplicity, flexibility and sc…
Controller-based Energy-Aware Wireless Sensor Network Routing using Quantum Algorithms
Jie Chen, Prasanna Date, Nicholas Chancellor +2
Energy efficient routing in wireless sensor networks has attracted attention from researchers in both academia and industry, most recently motivated by the opportunity to use SDN (…
Neuromorphic Computing is Turing-Complete
Prasanna Date, Catherine Schuman, Bill Kay +1
Neuromorphic computing is a non-von Neumann computing paradigm that performs computation by emulating the human brain. Neuromorphic systems are extremely energy-efficient and known…
Quantum Discriminator for Binary Classification
Prasanna Date, Wyatt Smith
Quantum computers have the unique ability to operate relatively quickly in high-dimensional spaces -- this is sought to give them a competitive advantage over classical computers.…
Training Deep Neural Networks with Constrained Learning Parameters
Prasanna Date, Christopher D. Carothers, John E. Mitchell +2
Today's deep learning models are primarily trained on CPUs and GPUs. Although these models tend to have low error, they consume high power and utilize large amount of memory owing…
Adiabatic Quantum Optimization Fails to Solve the Knapsack Problem
Lauren Pusey-Nazzaro, Prasanna Date
In this work, we attempt to solve the integer-weight knapsack problem using the D-Wave 2000Q adiabatic quantum computer. The knapsack problem is a well-known NP-complete problem in…
Balanced k-Means Clustering on an Adiabatic Quantum Computer
Davis Arthur, Prasanna Date
Adiabatic quantum computers are a promising platform for approximately solving challenging optimization problems. We present a quantum approach to solving the balanced -means cl…
QUBO Formulations for Training Machine Learning Models
Prasanna Date, Davis Arthur, Lauren Pusey-Nazzaro
Training machine learning models on classical computers is usually a time and compute intensive process. With Moore's law coming to an end and ever increasing demand for large-scal…
Adiabatic Quantum Linear Regression
Prasanna Date, Thomas Potok
A major challenge in machine learning is the computational expense of training these models. Model training can be viewed as a form of optimization used to fit a machine learning m…
Hyperparameter Optimization in Binary Communication Networks for Neuromorphic Deployment
Maryam Parsa, Catherine D. Schuman, Prasanna Date +8
Training neural networks for neuromorphic deployment is non-trivial. There have been a variety of approaches proposed to adapt back-propagation or back-propagation-like algorithms…