Expert Insights - Embedded

Exchange frameworks are emerging to make it easier for neural-network developers to swap between development environments.

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.

An introduction to how virtual emulation has fueled the application of co-modeling for complex design verification.

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.

Using specialised processors to implement key AI computation tasks such as CNNs.

The challenge for designers is to find ways of providing high levels of security in low-cost devices that have become worthwhile targets because of their role as gateways to more valuable information.

The assumption has been that extra security eats into profit margins. But with some lateral thinking it can actually improve the bottom line.

Using VESA's Display Stream Compression (DSC) standard to enable visually lossless performance and low latency for ultra-high-definition displays.