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.
CCIX is a cache coherency protocol, based on PCI Express, for interconnecting high-performance heterogenous multiprocessing systems.
Quadrupling the performance of a dedicated CNN engine within an embedded vision processing core brings more complex graph processing within reach.
SoC developers who want to use USB Type-C in their designs will have to implement HDCP 2.2 content protection so that the target devices will be able to play protected content.
The increasing complexity of human-machine interfaces is challenging processor designers to produce the necessary performance within a limited power budget
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