Open the closet of any gadget geek or computer nerd, and you’re likely to find a lot of skeletons. Stacked deep in a cardboard box or Tupperware tub, there they are: The remains of webcams, routers, phones and other devices deemed too obsolete to keep using and left to rot, metaphorically speaking, until they eventually find their way to a Best Buy recycling bin.
However, an under-the-radar startup called TeraDeep has developed a way to revive at least a few of those old devices by giving them the power of deep learning. The company has built a module that it calls the CAMCUE, which runs on an ARM-based processor and is designed to plug into other gear and run deep neural network algorithms on the inputs they send through. It could turn an old webcam into something with the smart features of a Dropcam, if not smarter.
“You can basically turn our little device into anything you want,” said TeraDeep co-founder and CTO Eugenio Culurciello during a recent interview. That potential is why the company won a Structure Data award as one of most-promising startups to launch in 2014, and will be presenting at our Structure Data conference in March.
But before TeraDeep can start transforming the world’s dumb gear into smart gear, the company needs to grow — a lot. It’s headquartered in San Mateo, California, and is the brainchild of Culurciello, who moonlights as an associate professor of engineering at Purdue University in Indiana. It has 10 employees, only three of which are full-time. It has a prototype of the CAMCUE, but isn’t ready to start mass-producing the modules and getting them into developers’ hands.
I recently saw a prototype of it at a deep learning conference in San Francisco, and was impressed by its how well it worked, albeit in a simple use case. Culurciello hooked the CAMCUE up to a webcam and to a laptop, and as he panned the camera, the display on the computer screen would alert the presence of a human when I was in the shot.
“As long as you look human-like, it’s going to detect you,” he said.
The prototype system can be set to detect a number of objects, including iPhones, which it was able to do when the phone was held vertically.
TeraDeep also has developed a web application, software libraries and a cloud platform that Culurciello said should make it fairly easy for power users and application developers, initially, and then perhaps everyday consumers to train TeraDeep-powered devices to do what they want them to do. It could be “as easy as uploading a bunch of images,” he said.
“You don’t need to be a programmer to make these things do magic,” TeraDeep CEO Didier Lacroix added.
But Culurciello and Lacroix have bigger plans for the company’s technology — which is the culmination of several years of work by Culurciello to develop specialized hardware for neural network algorithms — than just turning old webcams into smarter webcams. They’d like the company to become a platform player in the emerging artificial intelligence market, selling embedded hardware and software to fulfill the needs of hobbyists and large-scale device manufacturers alike.
It already has a few of the pieces in place. Aside from the CAMCUE module, which Lacroix said will soon shrink to about the surface area of a credit card, the company has also tuned its core technology (called nn-x, or neural network accelerator) to run on existing smartphone platforms. This means developers could build mobile apps that do computer vision at high speed and low power without relying on GPUs.
TeraDeep has also worked in system-on-a-chip design for partners that might want to embed more computing power into their devices. Think drones, cars and refrigerators, or smart-home gadgets a la the Amazon Echo and Jibo that rely heavily on voice recognition.
Lacroix said all the possibilities, and the interest it has received from folks who’ve seen and heard about the technology, are great, but noted that it might lead such a small company to suffer from a lack of focus or perhaps option paralysis.
“It’s overwhelming. We are a small company, and people get very excited,” he said. “… We cannot do everything. That’s a challenge for us.”