898 citations · 1.2k across the 19 of their papers we have counts for
5 papers · 1 filter
Linear-Complexity Relaxed Word Mover's Distance with GPU Acceleration
Kubilay Atasu, Thomas Parnell, Celestine Dünner +6
The amount of unstructured text-based data is growing every day. Querying, clustering, and classifying this big data requires similarity computations across large sets of documents…
Neuromorphic computing with multi-memristive synapses
Irem Boybat, Manuel Le Gallo, S. R. Nandakumar +7
Neuromorphic computing has emerged as a promising avenue towards building the next generation of intelligent computing systems. It has been proposed that memristive devices, which…
Efficient Use of Limited-Memory Accelerators for Linear Learning on Heterogeneous Systems
Celestine Dünner, Thomas Parnell, Martin Jaggi
We propose a generic algorithmic building block to accelerate training of machine learning models on heterogeneous compute systems. Our scheme allows to efficiently employ compute…
Temporal correlation detection using computational phase-change memory
Abu Sebastian, Tomas Tuma, Nikolaos Papandreou +4
For decades, conventional computers based on the von Neumann architecture have performed computation by repeatedly transferring data between their processing and their memory units…
Large-Scale Stochastic Learning using GPUs
Thomas Parnell, Celestine Dünner, Kubilay Atasu +2
In this work we propose an accelerated stochastic learning system for very large-scale applications. Acceleration is achieved by mapping the training algorithm onto massively paral…