With faster processors, more functions, and higher bandwidth than ever before, today’s avionics are pushing the cooling technology envelope to the breaking point – and beyond.

Which is better for machine-learning applications: 2nd Generation Intel Xeon Scalable Processors or Nvidia’s T4? A head-to-head comparison helps explain whether a CPU or a GPU is best for your high-performance ML solution.

New GPGPU modules expand Curtiss-Wright’s family of high-performance embedded computing (HPEC) processors for artificial intelligence (AI) applications.

Until recently, the use of moving liquid to remove excess heat from embedded computing processors was considered an expensive and unreliable luxury, but escalating demand and maturing technologies finally are putting this technology within reach.

The use of artificial intelligence (AI) based machine learning technologies in autonomous vehicles is on the rise.

Airbus aircraft links integration management system used in the demonstration is based on a safety certifiable rugged COTS processor system supplied by Curtiss-Wright.

Demo highlights DO-178 safety-certifiable VxWorks® 653 3.0.1.1 RTOS on Curtiss-Wright VPX3-152 SBC and graphics modules and cost-effective development path

DDC-I’s Deos DO-178 DAL Level A safety-critical multi-core real-time operating system (RTOS) will be demonstrated for the first time on Curtiss-Wright’s NXP Power Architecture QorIQ Quad-core AltiVec-enabled T2080 processor-based VPX3-152.