How to use machine learning for embedded vision—and many other embedded applications

2017年3月31日 | By News | Filed in: News.


Image3.jpg Adam Taylor and Xilinx’s Sr. Product Manager for SDSoC and Embedded Vision Nick Ni have just published an article on the EE News Europe Web site titled “Machine learning in embedded vision applications.” That title’s pretty self-explanatory, but there are a few points I’d like to highlight. Then you can go read the full article yourself.


As the article states, “Machine learning spans several industry mega trends, playing a very prominent role within not only Embedded Vision (EV), but also Industrial Internet of Things (IIoT) and Cloud Computing.” In other words, if you’re designing embedded products for any embedded market, you might well find yourself at a competitive disadvantage if you’re not adding machine-learning features to your road map.


This article closely ties machine learning with neural networks (including Feed-forward Neural Networks (FNNs), Recurrent Neural Networks (RNNs), and Deep Neural Networks (DNNs), and Convolutional Neural Networks (CNNs)). Neural networks are not programmed; they’re trained. Then, if they’re part of an embedded design, they’re deployed. Training is usually done using floating-point neural-network implementations but, for efficiency (power and cost), deployed neural networks can use fixed-point representations with very little or no loss of accuracy. (See “Counter-Intuitive: Fixed-Point Deep-Learning Inference Delivers 2x to 6x Better CNN Performance with Great Accuracy.”)


The programmable logic inside of Xilinx FPGAs, Zynq SoCs, and Zynq UltraScale+ MPSoCs is especially good at implementing fixed-point neural networks, as described in this article by Nick Ni and Adam Taylor. (Go read the article!)


Meanwhile, this is a good time to remind you of the recent Xilinx introduction of the reVISION stack for neural network development using Xilinx All Programmable devices. For more information about the Xilinx reVISION stack, see:

















via Xcell Daily Blog articles

March 30, 2017 at 07:24PM


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