The number of connected devices and sensors deployed at the edge today is growing, and so is the demand for solutions that excel at managing data-intensive tasks, promoting adaptable requirements, integrating advanced processing capabilities, and accelerating time to market.

It’s clear that in today’s fast-paced technological landscape, there’s a growing need for AI that’s not going anywhere anytime soon. The demand for real-time preprocessing, AI inferencing, and enhanced postprocessing is paramount to the acceleration of embedded AI applications like autonomous driving, industrial PCs, detection and tracking, 3D imaging, and more.

In the world of radio frequency (RF) applications, you need flexible, precise, and fast signal analysis over the widest possible spectrum to ensure compliance and reliability, especially in radar, signal intelligence, and test & measurement applications.

An interview with Sumit Shah, Senior Director, Adaptive & Embedded Computing Group, AMD

As the need for faster, more efficient data processing increases, Field Programmable Gate Arrays (FPGAs) are a fundamental technology bridging the gap between traditional hardware accelerators and cutting-edge applications like Artificial Intelligence (AI). They are becoming a critical building block for innovations from edge computing to cloud infrastructure.

Today’s growing number of FPGA-based applications, such as industrial networking, embedded vision, and automotive systems, can benefit from evaluation and prototyping tools that help developers bring low-power, cost-effective, and flexible solutions to market faster.

Intel and AMD have launched an x86 ecosystem advisory group that the companies say will bring together technology leaders to shape the future of the x86 architecture. They want to show that x86 is able to meet the needs of the market better than other architectures through performance and interoperability across hardware and software.