Cameras for the Internet of Things will have to be fast, cheap, and powerful—and might not look like cameras at all

["Dave Farber" <farber () gmail com>]

> Begin forwarded message:
>
> From: Warren Gifford <warrensgifford () gmail com>
> Subject: Cameras for the Internet of Things will have to be fast, cheap, and powerful—and might not look like cameras 
> at all
> Date: November 14, 2018 at 10:32:36 AM GMT+9
> To: 
>
> https://spectrum.ieee.org/telecom/wireless/the-iot-needs-a-new-set-of-eyes 
> <https://spectrum.ieee.org/telecom/wireless/the-iot-needs-a-new-set-of-eyes>
>
> The rise of computer vision has given us robot chefs 
> <https://www.theverge.com/2018/7/11/17550848/future-of-dining-automation-robotics-creator-sf-robot-burgers-ai> and 
> cameras that detect gas flares in fuel production. It’s also led to an increase in connected cameras that are trying 
> to run at the edge of the network.
>
> “Running at the edge” means these cameras are not only communicating wirelessly with the cloud but also communicating 
> with local gateways and working with built-in logic boards to complete a task. The task might be as simple as 
> notifying a manufacturer when a production line produces a defective item or as complex as identifying a person to 
> determine if the system should sound an alarm.
>
> But as we connect more cameras and ask them to perform more complicated tasks, their fundamental architecture is 
> changing. Today we see changes in the silicon that handles image processing and computing. In a few years, we may see 
> our notion of cameras change to meet the needs of digital eyes, not human ones.
>
> There are two challenges driving the silicon shift. First, processing power: Many of these cameras try to identify 
> specific objects by using machine learning. For example, an oil company might want a drone that can identify leaks as 
> it flies over remote oil pipelines. Typically, training these identification models is done in the cloud because of 
> the enormous computing power required. Some of the more ambitious chip providers believe that in a few years, not 
> only will edge-based chips be able to match images using these models, but they will also be able to train models 
> directly on the device.
>
> That’s not happening yet, due to the second challenge that silicon providers face. Comparing images with models 
> requires not just computing power but actual power. Silicon providers are trying to build chips that sip power while 
> still doing their job. Qualcomm has one such chip, called Glance 
> <https://www.qualcomm.com/invention/research/projects/computer-vision/always-on>, in its research labs. The chip 
> combines a lens, an image processor, and a Bluetooth radio on a module smaller than a sugar 
> <https://www.technologyreview.com/s/603964/qualcomm-wants-your-smartphone-to-have-energy-efficient-eyes/> cube.
>
> Glance can manage only three or four simple models, such as identifying a shape as a person, but it can do it using 
> fewer than 2 milliwatts of power. Qualcomm hasn’t commercialized this technology yet, but some of its latest 
> computer-vision chips combine on-chip image processing with an emphasis on reducing power consumption.
>
> But does a camera even need a lens? Researchers at the University of Utah suggest not, having invented a lensless 
> camera 
> <https://spectrum.ieee.org/tech-talk/computing/software/a-lensless-camera-built-specially-for-ai-and-computer-vision-programs-sorry-humans>
>  that eliminates some of a traditional camera’s hardware and high data rates. Their camera is a photodetector against 
> a pane of plexiglass that takes basic images and converts them into shapes a computer can be trained to recognize.
>
> This won’t work for jobs where high levels of detail are important, but it could provide a cheaper, more 
> power-efficient view of the world for computers fulfilling basic functions. We can also apply this thinking to how we 
> generate image data for computers. Researchers at the University of Washington, for example, have been studying ways 
> to use disruptions in Wi-Fi signals 
> <https://spectrum.ieee.org/tech-talk/computing/networks/internet-of-things-talks-by-turning-reflective> to teach 
> computers how to understand gestures.
>
> A camera doesn’t have to look like a camera anymore. It just needs to match incoming data to a statistical model to 
> tell us what something looks like. If it can do this cheaply and without sucking up too much power, it could change 
> beyond our recognition, even as it becomes more essential.
>
> This article appears in the November 2018 print issue as “New Eyes for the IoT.”




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