Secret behind Tesla Autopilot: NVIDIA
Tesla is one of the leading companies in the field of electric vehicles and autonomous driving. Tesla’s Autopilot system is a semi-autonomous driver assistance feature that can perform tasks such as lane keeping, adaptive cruise control, automatic lane changing, self-parking, and summoning the car from a parking spot. Tesla’s ultimate goal is to achieve full self-driving capability, where the car can drive itself without human intervention in any situation.
But how does Tesla Autopilot work? What is the secret behind its impressive performance and continuous improvement? The answer is NVIDIA.
NVIDIA GPUs: The Brain of Tesla Autopilot
Tesla Autopilot uses NVIDIA GPUs (graphics processing units) to power its deep neural networks, which are the core of its artificial intelligence system. NVIDIA GPUs are specialized hardware devices that can perform parallel computations at high speed and efficiency, making them ideal for running complex machine-learning algorithms.
Tesla has been using NVIDIA GPUs since the first generation of its Autopilot hardware, which was introduced in 2014. The first generation used an NVIDIA Tegra 3 processor, which had four CPU cores and 12 GPU cores. The second generation, which was launched in 2016, used an NVIDIA Drive PX 2 platform, which had two CPUs and two GPUs, delivering 24 trillion operations per second.
However, Tesla decided to develop its own custom chip for the third generation of its Autopilot hardware, which was unveiled in 2019. The Tesla FSD (full self-driving) chip is designed specifically for autonomous driving applications and has 6 billion transistors, 12 CPU cores, and 96 GPU cores. The chip can process 36 tera operations per second, which is 21 times faster than the previous NVIDIA platform.
But that does not mean that Tesla has abandoned NVIDIA completely. In fact, Tesla still relies on NVIDIA GPUs for training its deep neural networks on a massive scale.
NVIDIA A100: The Powerhouse of Tesla Supercomputer
Tesla uses an in-house supercomputer that is powered by 8x NVIDIA A100 Tensor Core GPUs to train its deep neural networks for Autopilot and self-driving capabilities. The supercomputer, which is named Dojo, is one of the most powerful in the world, ranking fifth on the TOP500 list of supercomputers as of June 2021.
The NVIDIA A100 Tensor Core GPU is the flagship product of NVIDIA’s Ampere architecture, which was launched in 2020. The A100 GPU has 54 billion transistors, 6,912 CUDA cores, and 432 Tensor cores. The A100 GPU can deliver up to 312 teraFLOPS of FP16 performance, which is 20 times faster than the previous generation of NVIDIA GPUs.
The A100 GPU also supports multi-instance GPU (MIG) technology, which allows a single GPU to be partitioned into up to seven independent instances, each with its own memory, cache, and compute resources. This enables multiple neural networks to run simultaneously on a single GPU, maximizing its utilization and efficiency.
Tesla uses the NVIDIA A100 GPUs to train its neural networks on a large dataset of real-world driving data collected from its fleet of vehicles. The dataset contains billions of images and videos from various cameras and sensors mounted on the cars. The neural networks learn from this data how to perceive the environment, plan the optimal path, and execute the appropriate actions.
Tesla claims that its supercomputer can train a neural network with one trillion parameters, which is an order of magnitude larger than the current state-of-the-art models. This allows Tesla to create more accurate and robust models that can handle complex and diverse scenarios.
Tesla Autopilot is a remarkable feat of engineering and innovation that leverages the power of NVIDIA GPUs. NVIDIA GPUs are the brain of Tesla Autopilot, enabling it to run sophisticated deep neural networks that can perform various driving tasks. NVIDIA GPUs are also the powerhouse of the Tesla Supercomputer, enabling it to train massive neural networks on a huge dataset of real-world driving data. Together, Tesla and NVIDIA are pushing the boundaries of artificial intelligence and autonomous driving.
