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.
This white paper discusses how ultrasonic non-destructive testing (NDT) can be efficiently and effectively optimized, thus reducing costs and risks, with the use of accurate engineering simulations.
This whitepaper discusses how RF MEMS acoustic resonator-based filters can be efficiently and effectively designed, thereby reducing cost, risk, and time to market.
In this whitepaper, we describe the virtual prototyping and beamforming optimization of a 110 x 56 PMUT array fingerprint sensor first designed and prototyped by Horsley et al at the Berkeley Sensor & Actuator Center.
In collaboration with Mentor, OnScale presents this essential paper on the product design of the MEMS pressure sensor.
Can you imagine what you would do with seemingly infinite, on-demand computational power?
5G is the world's next step into cellular mobile communications with high data rates and reduced latency, but are we going as fast we can to achieve it?
