Thomas Potok

Caspian provides a development platform for neuromorphic algorithms and applications research.  The C++ and Python APIs allow for quick and flexible development of new algorithms and…

Deep learning is actively used in a wide range of fields for scientific discovery. To effectively apply deep learning to a particular problem, it is important to select an appropriate network…

In this work, the researchers introduced a Bayesian approach for optimizing the hyperparameters of an algorithm for training binary communication networks that can be deployed to neuromorphic…

Designing and training an appropriate spiking neural network for neuromorphic deployment remains an open challenge in neuromorphic computing. In 2016, researchers from ORNL introduced an approach for…

Designing spiking neural networks for neuromorphic deployment is a non-trivial task.  It is further complicated when there are resource constraints for the neuromorphic implementation, such as…

Designing a neuromorphic computing system involves selection of several hyperparameters that not only affect the accuracy of the framework, but also the energy efficiency and speed of inference and…

Neuromorphic architectures are designed and developed with the goal of having small, low power chips that can perform control and machine learning tasks. However, the power consumption of the…

In this work, we discuss an approach for that utilizes high-performance computing (HPC) to evolve the hyperparameters and topology of convolutional neural networks in order to investigate the ability…

Our deep learning framework is called Multinode Evolutionary Neural Networks for Deep Learning (MENNDL).  MENNDL relies on two optimization methods, genetic algorithms and support vector…