3 papers
cs.NE2023
Harnessing Manycore Processors with Distributed Memory for Accelerated Training of Sparse and Recurrent Models
Jan Finkbeiner, Thomas Gmeinder, Mark Pupilli +2
Current AI training infrastructure is dominated by single instruction multiple data (SIMD) and systolic array architectures, such as Graphics Processing Units (GPUs) and Tensor Pro…
physics.comp-ph2023
Generating Minimal Training Sets for Machine Learned Potentials
Jan Finkbeiner, Samuel Tovey, Christian Holm
This letter presents a novel approach for identifying uncorrelated atomic configurations from extensive data sets with a non-standard neural network workflow known as random networ…
cs.NE2023
Online Transformers with Spiking Neurons for Fast Prosthetic Hand Control
Nathan Leroux, Jan Finkbeiner, Emre Neftci
Transformers are state-of-the-art networks for most sequence processing tasks. However, the self-attention mechanism often used in Transformers requires large time windows for each…