This white paper examines the key challenges multi-core processors present in safety-certifiable systems, and the traditional and newer, more effective techniques for alleviating them to ensure MCP-based systems will safely perform their intended functions.

Mission management systems that meet ever-present size, weight, and power (SWaP) demands while providing the scalability and flexibility to handle all of the mission tasks today’s warfighter may encounter.

Whether looking for a modular, SFF LRU or a scalable VPX system designed to meet your program requirements, Curtiss-Wright computing systems are built rugged to perform optimally in the harshest deployed environments on land, in the air, or at sea.

Discover how building a system with a dissimilar redundant architecture can help overcome the challenges of meeting stringent DO-254/178 DAL A safety requirements for aviation applications.

COTS products and open-architecture solutions for mission computing, signal processing, graphics, communication fabrics, system and sensing I/O, and data storage.

This essential guide describes MOSA's significance for defense technology and the roles standards like SOSA, CMFF, CMOSS, VICTORY, GVA, and FACE play.

This white paper was presented at the 2021 NDIA Ground Vehicle Systems Engineering and Technology Symposium, Vehicle Electronics Architectures Technical Session, August 10-12, 2021.

Tensor cores are indispensable for performing the types of calculations needed for artificial intelligence (AI) and machine learning. The role of AI and machine learning in defense applications is on the rise, making tensor cores critical for defense.

Like humans, machines learn from experience. They make observations from inputs of images, text, or other data, and then look for patterns. After the machine runs through the mathematical layers, it learns to make better decisions based on the examples it was given.

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 you should choose a CPU or a GPU for your high-performance machine learning solution.