80 citations
- Massachusetts Institute of TechnologyUS2 papers
- Princeton UniversityUS2 papers
- Birla Institute of Technology and Science, PilaniIN1 paper
- Carnegie Mellon UniversityUS1 paper
- Cerebral Diagnostics (Canada)CA1 paper
- Columbia UniversityUS1 paper
- Duke UniversityUS1 paper
- ETH ZurichCH1 paper
- European Organization for Nuclear ResearchCH1 paper
- Fermi National Accelerator LaboratoryUS1 paper
- Google DeepMind (United Kingdom)GB1 paper
- Google (United States)US1 paper
9 papers
Beyond Exascale: Dataflow Domain Translation on a Cerebras Cluster
Tomas Oppelstrup, Nicholas Giamblanco, Delyan Z. Kalchev +5
Simulation of physical systems is essential across scientific and engineering domains. Commonly used domain decomposition methods are unable to simultaneously deliver both high sim…
Near-Optimal Wafer-Scale Reduce
Piotr Luczynski, Lukas Gianinazzi, Patrick Iff +3
Efficient Reduce and AllReduce communication collectives are a critical cornerstone of high-performance computing (HPC) applications. We present the first systematic investigation…
Wafer-Scale Fast Fourier Transforms
Marcelo Orenes-Vera, Ilya Sharapov, Robert Schreiber +3
We have implemented fast Fourier transforms for one, two, and three-dimensional arrays on the Cerebras CS-2, a system whose memory and processing elements reside on a single silico…
Efficiently Disentangle Causal Representations
Yuanpeng Li, Joel Hestness, Mohamed Elhoseiny +2
This paper proposes an efficient approach to learning disentangled representations with causal mechanisms based on the difference of conditional probabilities in original and new d…
Applications and Techniques for Fast Machine Learning in Science
Allison McCarn Deiana, Nhan Tran, Joshua Agar +84
In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time…
Memory Efficient 3D U-Net with Reversible Mobile Inverted Bottlenecks for Brain Tumor Segmentation
Mihir Pendse, Vithursan Thangarasa, Vitaliy Chiley +3
We propose combining memory saving techniques with traditional U-Net architectures to increase the complexity of the models on the Brain Tumor Segmentation (BraTS) challenge. The B…