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.
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.
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