no code implementations • 16 Apr 2024 • Adarsha Balaji, Ramyad Hadidi, Gregory Kollmer, Mohammed E. Fouda, Prasanna Balaprakash
Our NAS and HPS of (1) BraggNN achieves a 31. 03\% improvement in bragg peak detection accuracy with a 87. 57\% reduction in model size, and (2) PtychoNN achieves a 16. 77\% improvement in model accuracy and a 12. 82\% reduction in model size when compared to the baseline PtychoNN model.
no code implementations • 13 Nov 2020 • Ramyad Hadidi, Jiashen Cao, Michael S. Ryoo, Hyesoon Kim
Satisfying the high computation demand of modern deep learning architectures is challenging for achieving low inference latency.
no code implementations • 13 Mar 2020 • Ramyad Hadidi, Bahar Asgari, Jiashen Cao, Younmin Bae, Da Eun Shim, Hyojong Kim, Sung-Kyu Lim, Michael S. Ryoo, Hyesoon Kim
To benefit from available compute resources with low communication overhead, we propose the first DNN parallelization method for reducing the communication overhead in a distributed system.
no code implementations • 8 Jan 2019 • Ramyad Hadidi, Jiashen Cao, Micheal S. Ryoo, Hyesoon Kim
In this paper, we propose an approach that utilizes aggregated existing computing power of Internet of Things (IoT) devices surrounding an environment by creating a collaborative network.
no code implementations • 5 Feb 2018 • Ramyad Hadidi, Jiashen Cao, Matthew Woodward, Michael S. Ryoo, Hyesoon Kim
Furthermore, in image recognition, Musical Chair achieves similar performance and saves dynamic energy.