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.

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.

This white paper describes how modern AI technology can benefit the warfighter as embedded solutions at the network edge are deployed for military and aerospace platforms. Edge computing brings data storage and computation closer to where it's needed.

System developers must safeguard tactical information exchange and the integrity of command and control links between ground stations and airborne platforms.

This white paper reviews popular processor architectures to help you make an informed decision when defining your electronics payload.

This white paper explores the performance and SWaP benefits of multicore processors and their challenges in safety-critical applications, and how to overcome them.

This white paper examines the key challenges multi-core processors (MCPs) present in safety-certifiable systems, as well as techniques for alleviating them to ensure MCP-based systems will safely perform their intended functions.

In the third paper in our series, we cover additional features of the Dolphin PCIe Fabric Communications Library that help minimizes latency, maximize system bandwidth, and optimize configuration flexibility.

In this second white paper in our two-part series, we present several different software interfaces provided for applications development, comparing their advantages and tradeoffs. Finally, we present performance benchmarks using a variety of Curtiss-Wright.

We introduce the use of fabrics for high-performance embedded systems and focus on the hardware and architectural options available to the systems designer.