H2PIPE: High throughput CNN Inference on FPGAs with High-Bandwidth Memory

no code implementations • 17 Aug 2024 • Mario Doumet, Marius Stan, Mathew Hall, Vaughn Betz

Convolutional Neural Networks (CNNs) combine large amounts of parallelizable computation with frequent memory access.

Flame Stability Analysis of Flame Spray Pyrolysis by Artificial Intelligence

no code implementations • 22 Oct 2020 • Jessica Pan, Joseph A. Libera, Noah H. Paulson, Marius Stan

This research has the potential to autonomously track and manage flame spray pyrolysis as well as other flame technologies by monitoring and classifying the flame stability.

BIG-bench Machine Learning

Towards Online Steering of Flame Spray Pyrolysis Nanoparticle Synthesis

1 code implementation • 16 Oct 2020 • Maksim Levental, Ryan Chard, Joseph A. Libera, Kyle Chard, Aarthi Koripelly, Jakob R. Elias, Marcus Schwarting, Ben Blaiszik, Marius Stan, Santanu Chaudhuri, Ian Foster

Flame Spray Pyrolysis (FSP) is a manufacturing technique to mass produce engineered nanoparticles for applications in catalysis, energy materials, composites, and more.

An Experimentally Driven Automated Machine Learned lnter-Atomic Potential for a Refractory Oxide

1 code implementation • 9 Sep 2020 • Ganesh Sivaraman, Leighanne Gallington, Anand Narayanan Krishnamoorthy, Marius Stan, Gabor Csanyi, Alvaro Vazquez-Mayagoitia, Chris J. Benmore

Understanding the structure and properties of refractory oxides are critical for high temperature applications.

BIG-bench Machine Learning

Machine Learning Inter-Atomic Potentials Generation Driven by Active Learning: A Case Study for Amorphous and Liquid Hafnium dioxide

2 code implementations • 22 Oct 2019 • Ganesh Sivaraman, Anand Narayanan Krishnamoorthy, Matthias Baur, Christian Holm, Marius Stan, Gabor Csányi, Chris Benmore, Álvaro Vázquez-Mayagoitia

We apply this scheme to a Hafnium dioxide (HfO2) dataset generated from a melt-quench ab initio molecular dynamics (AIMD) protocol.

Active Learning Bayesian Optimization