363 citations · 455 across the 10 of their papers we have counts for
11 papers
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies
Shuaiwen Leon Song, Bonnie Kruft, Minjia Zhang +89
In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences. This could herald a new era of scient…
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…
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…