re 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: John Day <jeanjour () comcast net>
> Date: November 15, 2018 at 10:17:09 PM GMT+9
> To: Dave Farber <dave () farber net>
> Subject: Re: [IP] Cameras for the Internet of Things will have to be fast, cheap, and powerful—and might not look 
> like cameras at all
>
> Could someone explain to me what a camera (or really anything) not "at the edge" would be?  A couple of examples 
> would help.
>
> Just trying to understand the term.
>
> Thanks,
> John
>
> > On Nov 13, 2018, at 20:40, Dave Farber <farber () gmail com> wrote:
> >
> >
> >
> > > 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
> > >
> > > The rise of computer vision has given us robot chefs 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, in its research labs. The chip combines a 
> > > lens, an image processor, and a Bluetooth radio on a module smaller than a sugar 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 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 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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