Industry Expert Blogs

The term "deep learning" refers to using deep (multi-layer) artificial neural networks to make sense out of complex data such as images, sounds, and text. Until recently, this technology has been largely relegated to academia. Over the past couple of years, however, increased computing performance coupled with reduced power consumption and augmented by major strides in neural network frameworks and algorithms has thrust deep learning into the mainstream.

When I attended the Embedded Vision Summit recently, for example, I saw an amazing demonstration of machine vision in which a deep neural network (DNN) running on an FPGA was identifying randomly presented images in real time (check out this column to see a video). As an aside, one of the best lines I heard at the summit was "You can't swing a dead cat in here without some deep learning system saying 'Hey, that's a dead cat!'" But we digress...

As another example, take a look at this column describing how researchers at MIT used a deep learning algorithm to analyze videos showing tens of thousands of different objects and materials being prodded, scraped, and hit with a drumstick. The trained algorithm could subsequently watch silent videos and generate accompanying sounds sufficiently convincing to fool human observers.

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