Expert Insights

The computational and algorithmic demands made by computer vision systems highlight HLS' value for AI system development.

Bob Smith of the ESD Alliance describes how we can promote the ongoing evolution of the design ecosystem.

The basics of USB 3.2, how to implement it in an SoC, and how USB Type-C connectors and cables are used in USB 3.2 systems.

As AI becomes pervasive in computing applications, so too does the need for high-grade security in all levels of the system.

Machine-learning strategies for embedded vision are evolving so quickly that designers need access to flexible, heterogenous processor architectures that can adapt as the algorithms evolve.

Dina Medhat describes what you need to know about the types of waiver strategy that can be applied.

Choosing the right crypto processor implementation involves a complex set of design tradeoffs between speed, area, power consumption and flexibility. Using consistent benchmarks can help explore your options.

Many car manufacturers are exploring the possibilities of autonomous vehicles. But what will it take to build sufficient AI performance into them to enable true autonomy?

Artificial intelligence and machine learning require the performance and flexibility offered by embedded FPGA (eFPGA) technology.

High-performance vision-processing algorithms need optimized CNN engines to deliver the right performance within the power budget of embedded applications.