The goal of reducing the cost of equipping Army vehicles with new capabilities, while adding increasing interoperability and flexibility of operation, can be accomplished through the use of a Modular Open Systems Approach (MOSA), a technical and business strategy for designing an affordable and adaptable system.

Designers of high-performance embedded computing modules and systems must understand the technical considerations required to realize the true benefits of 100G technology fully.

Building connected and workable communications in remote environments presents unique challenges, but the right technology can keep users connected in even contested and harsh environments.

Curtiss-Wright has developed Fabric100, a complete end-to-end ecosystem that features 100 Gigabit Ethernet (GbE) connectivity between rugged OpenVPX modules and system components.

The SOSA Technical Standard combines MOSA principles with security to enable secure sensor processing systems to be quickly developed and deployed in a low-risk environment.

This paper aims to put MOSA (Modular Open Systems Approach) into perspective and enable you, whether a defense contractor, a decision-maker at the DoD, or a public policymaker, to put the increasingly relevant MOSA to use better.

This white paper looks at whether Arm’s commercial success can translate to defense and aerospace markets in order to meet the highest DO-254 requirements and fill the Power Architecture void.

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.