142 citations · 284 across the 19 of their papers we have counts for
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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…
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…
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…
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…
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…