Elon Musk has promised a fleet of autonomous Tesla taxis by 2020. The company reportedly purchased a computer vision startup to help meet that goal.
What’s new: Tesla acquired DeepScale, a Silicon Valley startup that processes computer vision on low-power electronics, according to CNBC. The price was not reported.
- DeepScale, founded in 2015 by two UC Berkeley computer scientists, had raised nearly $19 million prior to Tesla’s purchase.
- The company’s platform, called Carver21, uses a high-efficiency neural network architecture known as SqueezeNet.
- The systems uses three parallel networks  to perform object detection, lane identification, and drivable area identification.
- Carver21 imposes a computational budget of 0.6 trillion operations per second. That’s a relatively small demand on Tesla’s custom chipset, which is capable of 36 trillion operations per second.
Behind the news: Tesla’s stock is down 25 percent this year due to manufacturing problems and a drop in demand for electric vehicles. In July, the company lost around 10 percent of its self-driving dev team after Musk expressed displeasure at their inability to adapt its highway-specific autopilot software to urban driving, according to a report in The Information. The recent debut of Tesla’s Smart Summon feature, which enables cars to drive themselves from a parking space to their waiting owner, was marred by reports of accidents.
Why it matters: Cars operate within tight constraints on electrical power, and self-driving cars consume lots of power-hungry processing. Tesla is betting that leaner processing will help it reach full autonomy within the power budget of an electric vehicle. Fleets of self-driving taxis would certainly bolster the company’s bottom line.
We’re thinking: Low-power processing is just one of many things that will make fully self-driving systems practical. There’s widespread skepticism about Tesla’s ability to deliver on its promises on time, but every piece will help.