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Morpho Provides “SoftNeuro” to Highly Resolved Galaxy Simulations Project for Universities: Supporting 3D Simulation with Supercomputer Fugaku

Morpho , a global leader in image processing and imaging AI solutions, announced that Morpho will provide its core product “SoftNeuro”, the world’s fastest deep learning inference engine, to a project promoted by the University of Tokyo, Tohoku University, and Kobe University to accelerate forecasting the expansion of supernova shells for highly resolved galaxy simulations using deep learning.
“SoftNeuro” will accelerate the inference of 3D simulations (galaxy formation simulations) using deep learning on the supercomputer Fugaku.

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“SoftNeuro” supports major deep learning frameworks and performs faster processing in various edge-device environments. Since it is a general-purpose inference engine, it can be used not only for image recognition but also for speech recognition and text analysis. Morpho has proposed and provided “SoftNeuro” for multi-platform and high-speed inference for various detection applications based on image data.

For this project, Morpho has realized 3D CNN inference acceleration on Fugaku (SVE optimization of Conv3D and application of 3D Winograd) through the original development of “SoftNeuro” for use in 3D simulations. Through the project and collaboration, Morpho will support further acceleration of 3D simulations (galaxy formation simulations) using deep learning on supercomputer Fugaku.

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The project is to accelerate highly resolved galaxy formation simulations.
We have developed the 3D-CNN-based deep learning model that predicts anisotropic shell expansion of supernova (SN) explosions and identifies particles with small timesteps. Our model is based on Memory-In-Memory Network (Wang et al. 2018), which consists of 2D-CNNs and predicts future images.

“Our 3D CNN-based deep learning model predicts the expansion of SN shells within N-body/SPH galaxy formation simulations. We use the prediction results to decrease communication (data transfer) overheads, and by doing so we improve the scalability of our galaxy formation simulations. In the past, our 3D-CNN-based deep learning models have required extremely long inference time compared to the calculation time of real simulations on the supercomputer Fugaku. However, “SoftNeuro” enables our model to infer the result faster, making it practical in galaxy formation simulations.”

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