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Using Synthetic Datasets to Train Embedded AI

This white paper discusses how synthetic datasets for training AI can be generated in hours using the OnScale cloud simulation platform. The demonstrated approach of using synthetic datasets to train AI networks can drastically reduce cost, risk, and time for the development of new hardware technologies.

With the OnScale cloud simulation platform, research and design engineers can run multiphysics simulations of many devices and products for generating synthetic datasets to train AI networks. Simulation studies run in OnScale include piezoelectricity, electrical circuits, structural mechanics, acoustics, and heat transfer phenomena.

OnScale is fully cloud-enabled, empowering engineers with the high-performance computing (HPC) resources needed to explore their design space quickly and with ease. Semiconductors, MEMS, sensors, medical devices, and 5G and IoT RF systems are among the many applications that can benefit from design and optimization with OnScale.

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