On Monday, Tesla held a major event
to show off the company's impressive progress toward full self-driving technology. The company demonstrated a new neural network computer that seems to be competitive with industry leader Nvidia. And Tesla explained how it leverages its vast fleet of customer-owned vehicles to collect data that helps the company train its neural networks. Elon Musk's big message was that Tesla was close to reaching the holy grail of fully self-driving cars. Musk predicts that by the end of the year, Tesla's cars will be able to navigate both surface streets and freeways, allowing them to drive between any two points without human input.
At this point, the cars will be "feature complete," in Musk's terminology, but will still need a human driver to monitor the vehicle and intervene in the case of a malfunction. But Musk predicts it will only take about six more months for the software to become reliable enough to no longer require human supervision. By the end of 2020, Musk expects Tesla to have thousands of Tesla vehicles providing driverless rides to people in an Uber-style taxi service. In other words, Musk seems to believe that once Tesla's cars become "feature complete" later this year, they will be 90 percent of the way to full autonomy. The big question is whether that's actually true-or whether it's only true in the Cargill sense.
Since 2016, Autopilot has been powered by Nvidia's Drive PX hardware. But last year we learned that Tesla was dumping Nvidia in favor of a custom-designed chip. Monday's event served as a coming-out party for that chip-officially known as the Full Self-Driving Computer.
Musk invited Pete Bannon, a chip designer Tesla hired away from Apple in 2016, to explain his work. Bannon said that the new system is designed to be a drop-in replacement for the previous Nvidia-based system.
"These are two independent computers that boot up and run their own operating systems," Bannon said. Each computer will have an independent source of power. If one of the computers crashes, the car will be able to continue driving.
Each self-driving chip has 6 billion transistors, Bannon said, and the system is designed to perform a handful of operations used by neural networks in a massively parallel way. Each chip has two compute engines capable of performing 9,216 multiply-add operations-the heart of neural network computations-every clock cycle. Each Full Self-Driving system will have two of these chips, resulting in a total computing capacity of 144 trillion operations per second.
Tesla says that's a 21-fold improvement over the Nvidia chips the company was using before. Of course, Nvidia has produced newer chips since 2016, but Tesla says that its chips are more powerful than even Nvidia's current Drive Xavier chip-144 TOPS compared to 21 TOPS.