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Embedded AI Processors: The Cambrian Explosion

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Half a billion years ago something remarkable occurred: an astonishing, sudden increase in new species of organisms. Paleontologists call it the Cambrian Explosion, and many of the animals on the planet today trace their lineage back to this event.

A similar thing is happening in processors for embedded vision and artificial intelligence (AI) today, and nowhere will that be more evident than at the Embedded Vision Summit, which will be an in–person event held in Santa Clara, California, from May 16–19. The Summit focuses on practical know–how for product creators incorporating AI and vision in their products. These products demand AI processors that balance conflicting needs for high performance, low power, and cost sensitivity. The staggering number of embedded AI chips that will be on display at the Summit underscores the industry’s response to this demand. While the sheer number of processors targeting computer vision and ML is overwhelming, there are some natural groupings that make the field easier to comprehend. Here are some themes we’re seeing.

Jeff Bier
Jeff Bier (Source: Embedded Vision Summit)

First, some processor suppliers are thinking about how to best serve applications that simultaneously apply machine learning (ML) to data from diverse sensor types — for example, audio and video. Synaptics’ Katana low–power processor, for example, fuses inputs from a variety of sensors, including vision, sound, and environmental. Xperi’s talk on smart toys for the future touches on this, as well.

Second, a subset of processor suppliers are focused on driving power and cost down to a minimum. This is interesting because it enables new applications. For example, Cadence will be presenting on additions to their Tensilica processor portfolio that enable always–on AI applications. Arm will be presenting low–power vision and ML use cases based on their Cortex–M series of processors. And Qualcomm will be covering tools for creating low–power computer vision apps on their Snapdragon family.

Third, although many processor suppliers are focused mainly or exclusively on ML, a few are addressing other kinds of algorithms typically used in conjunction with deep neural networks, such as classical computer vision and image processing.  A great example is quadric, whose new q16 processor is claimed to excel at a wide range of algorithms, including both ML and conventional computer vision.

Finally, an entirely new species seems to be coming to the fore: neuromorphic processors. Neuromorphic computing refers to approaches that mimic the way the brain processes information. For example, biological vision systems process events in the field of view, as opposed to classical computer vision approaches that typically capture and process all the pixels in a scene at a fixed frame rate that has no relation to the source of the visual information. The Summit’s keynote talk, “Event–based Neuromorphic Perception and Computation: The Future of Sensing and AI” by Prof. Ryad Benosman, will give an overview of the advantages to be gained by neuromorphic approaches. Opteran will be presenting on their neuromorphic processing approach to enable vastly improved vision and autonomy, the design of which was inspired by insect brains.

Whatever your application is, and whatever your requirements are, somewhere out there is an embedded AI or vision processor that’s the best fit for you. At the Summit, you’ll be able to learn about many of them, and speak with the innovative companies developing them.  Come check them out, and be sure to check back in 10 years — when we will see how many of 2032’s AI processors trace their lineage to this modern–day Cambrian Explosion!

Jeff Bier is the president of consulting firm BDTI, founder of the Edge AI and Vision Alliance, and the general chair of the Embedded Vision Summit.

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