9 citations · 16 across the 2 of their papers we have counts for
5 papers
Reduced Precision Strategies for Deep Learning: A High Energy Physics Generative Adversarial Network Use Case
Florian Rehm, Sofia Vallecorsa, Vikram Saletore +5
Deep learning is finding its way into high energy physics by replacing traditional Monte Carlo simulations. However, deep learning still requires an excessive amount of computation…
CP2K: An Electronic Structure and Molecular Dynamics Software Package -- Quickstep: Efficient and Accurate Electronic Structure Calculations
Thomas D. Kühne, Marcella Iannuzzi, Mauro Del Ben +36
CP2K is an open source electronic structure and molecular dynamics software package to perform atomistic simulations of solid-state, liquid, molecular and biological systems. It is…
High-Performance Deep Learning via a Single Building Block
Evangelos Georganas, Kunal Banerjee, Dhiraj Kalamkar +6
Deep learning (DL) is one of the most prominent branches of machine learning. Due to the immense computational cost of DL workloads, industry and academia have developed DL librari…
Anatomy Of High-Performance Deep Learning Convolutions On SIMD Architectures
Evangelos Georganas, Sasikanth Avancha, Kunal Banerjee +4
Convolution layers are prevalent in many classes of deep neural networks, including Convolutional Neural Networks (CNNs) which provide state-of-the-art results for tasks like image…
Machine Learning in High Energy Physics Community White Paper
Kim Albertsson, Piero Altoe, Dustin Anderson +125
Machine learning has been applied to several problems in particle physics research, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by a…