Accelerated computing transforms chipmaking
Summary
Since then, NVIDIA reinvented its computing stack for deep learning, opening up “multi trillion-dollar opportunities in robotics, autonomous vehicles and manufacturing,” Huang said. By offloading and accelerating compute-intensive algorithms, NVIDIA routinely speeds up applications by 10–100x while reducing power and cost by an order of magnitude, Huang explained. Companies like D2S, IMS Nanofabrication, and NuFlare build mask writers – machines that create photomasks, stencils that transfer patterns onto wafers – using electron beams. NVIDIA GPUs play a crucial role here, too, in processing classical physics modelling and deep learning to generate synthetic reference images and detect defects. Huang said scientists could explore hypotheses by testing them in the digital twin before activating the physical reactor, improving energy yield, predictive maintenance, and reducing downtime.