As AI becomes pervasive in computing applications, so too does the need for high-grade security in all levels of the system.
The proliferation of attacks against embedded systems is making designers realize that they need to do more to secure their products and ecosystems.
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.
Designers need to understand how the architecture of electronic control units used to implement ADAS in vehicles is changing.
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.
An evolution of the Ethernet standard enable time-sensitive networking with the predictable latencies and guaranteed bandwidth necessary for automotive applications.
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.
Increasing resolutions and rising frame rates are making it more challenging than ever to drive embedded displays effectively.
How to combine a display processing unit from one company and a MIPI Display Serial Interface solution from another to build 4K embedded displays for smartphones and AR/VR devices.
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