Neuromorphic computing offers one path forward for AI at the edge. However, accessing and effectively utilizing a neuromorphic hardware platform is non-trivial. In this work, researchers…
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…
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…