"To get rid of the dependency on the radar sensor for the pilot, we generated over 10 billion labels across two and a half million clips. And so to do that, we had to skill our huge offline neural networks and our simulation engine across 1000s of GPUs, and just a little bit shy of 20,000 CPU cores. On top of that, we also included over 2000, actual autopilot full self driving computers in the loop with our simulation engine. And that's our smallest compute cluster.
So I'd like to give you some idea of what it takes to take our neural networks and move them in the car. And so the the two main constraints that we're working on there here are mostly latency and framerate, which are very important for safety, but also to get proper estimates of acceleration and velocity all of our surroundings.
And so the meat of the problem really is around the AI compiler that we write and extend here within the group that essentially maps the compute operations from a pytorch model, to a set of dedicated and accelerated pieces of hardware. And we do that by figuring out a schedule that's optimized for throughput while working on very severe SRAM constraints.
And so by the way, we're not doing that just on one engine, but across two engines on the autopilot computer. And the way we use those engines here at Tesla is such that, at any given time, only one of them will actually output control commands to the vehicle, while the other one is used as an extension of compute. But those rules are interchangeable, both on the hardware and software level.
So how do we very quickly together as a group to this AI development cycles? Well, first, we have been scaling our capacity to evaluate our software neural network dramatically over the past few years. And today, we are running over a million evaluations per week on any code change that the team is producing. And those evaluations run on over 3000 actual full self driving computers that are hooked up together in a dedicated cluster."
"To get rid of the dependency on the radar sensor for the pilot, we generated over 10 billion labels across two and a half million clips. And so to do that, we had to skill our huge offline neural networks and our simulation engine across 1000s of GPUs, and just a little bit shy of 20,000 CPU cores. On top of that, we also included over 2000, actual autopilot full self driving computers in the loop with our simulation engine. And that's our smallest compute cluster.
So I'd like to give you some idea of what it takes to take our neural networks and move them in the car. And so the the two main constraints that we're working on there here are mostly latency and framerate, which are very important for safety, but also to get proper estimates of acceleration and velocity all of our surroundings.
And so the meat of the problem really is around the AI compiler that we write and extend here within the group that essentially maps the compute operations from a pytorch model, to a set of dedicated and accelerated pieces of hardware. And we do that by figuring out a schedule that's optimized for throughput while working on very severe SRAM constraints.
And so by the way, we're not doing that just on one engine, but across two engines on the autopilot computer. And the way we use those engines here at Tesla is such that, at any given time, only one of them will actually output control commands to the vehicle, while the other one is used as an extension of compute. But those rules are interchangeable, both on the hardware and software level.
So how do we very quickly together as a group to this AI development cycles? Well, first, we have been scaling our capacity to evaluate our software neural network dramatically over the past few years. And today, we are running over a million evaluations per week on any code change that the team is producing. And those evaluations run on over 3000 actual full self driving computers that are hooked up together in a dedicated cluster."