As a continuation of our series on Tesla’s patents, we’re taking a look at how Tesla optimizes the performance of AI - FSD, in this case - in autonomous vehicles and robots. Patent WO2024073115A1 goes over efficiently running complex AI models on specialized hardware.
Before we dive into this article, we recommend reading our article on How FSD Works and our other article on Tesla’s Universal Translator for streamlining FSD deployments. While they’re not necessary, the background knowledge will help you appreciate all the details behind how Tesla does their optimization.
Just like before, we’ll be breaking this down into sections and making them as easily understandable as possible.
AI Subnetworks
FSD isn’t a monolithic entity - it is composed of smaller, specialized sub-networks, each dedicated to a specific aspect or function of autonomous operation. This modular design means that Tesla can work on improving one or all sections through training. When one section is improved, the end-to-end nature of the AI also means that the other sections will learn to adapt to the improvements and, therefore, perform better. It also allows for more efficient processing and adaptability during deployment and initial platform training.
These sub-networks might be responsible for tasks such as:
Recognizing and interpreting traffic signals
Detecting and tracking moving objects including vehicles, pedestrians, cyclists, and more
Maintaining lane position and navigating roads
Generating 3D maps of the surrounding environment
Planning paths and making real-time driving decisions
This division of labor allows FSD to handle the complexities of autonomous driving with greater efficiency and precision
Tailored Compilers
Different hardware components are good at different things - and they also require different types of instructions. CPUs, GPUs, and specialized AI accelerators (NPUs) all have unique architecture and capabilities.
Tesla uses a compiler toolchain to translate FSD into machine code that is specifically tailored to each hardware component. This ensures that instructions are executed optimally on each processor, maximizing performance and efficiency.
Strategic Assignment
To further optimize performance, Tesla employs a system that intelligently assigns each FSD sub-network to the most suitable hardware component. This ensures that computationally demanding tasks are handled by the most powerful processors while simpler tasks are delegated to more efficient units.
This strategic assignment of tasks maximizes the overall efficiency of the system, ensuring that each component operates within its optimal performance range.
Optimized Scheduling
The order in which the hardware executes instructions also plays a crucial role in performance. Tesla's system includes an "execution scheduler" that determines the most efficient sequence of operations, minimizing delays and maximizing real-time responsiveness.
This optimized scheduling ensures that the FSD can react quickly and make informed decisions in dynamic driving situations - or quick-response situations with Optimus - like catching a ball.
While the demo here has been confirmed to be teleoperated, Tesla has said they’re working to let Optimus do this autonomously in the future.
Let’s give Optimus a hand for catching ball!
— Elon Musk (@elonmusk) November 28, 2024
pic.twitter.com/i44qcD1iLd
Quantization-Aware Training
To reduce the computational burden and power consumption of FSD, Tesla employs a technique called "quantization-aware training." This involves training FSD to work with lower-precision numbers, which require less processing power and memory. Essentially - rounding.
This approach allows the AI to operate efficiently without significantly compromising accuracy, striking a balance between performance and resource utilization.
Clock Synchronization
In hardware systems with multiple chips, maintaining precise timing is crucial for accurate and synchronized operation. Tesla's system incorporates mechanisms to synchronize the clocks of all processing units, preventing timing errors and ensuring seamless coordination between different components.
This precise clock synchronization is essential for FSD to make accurate real-time calculations and respond effectively to changing conditions.
Redundancy and Failover
To ensure reliability and safety, Tesla's system supports redundant hardware configurations. This means that if a critical component fails, a backup component can seamlessly take over, preventing disruptions in operation.
This redundancy and failover capability is crucial for maintaining the safety and integrity of autonomous systems, especially when driving. Tesla has built-in both physical and software redundancy to FSD, ensuring that it maintains a minimum standard of safety when operating autonomously.
In Simpler Terms…
Imagine a large company (FSD) with different departments (sub-networks) responsible for specific tasks. Each department has its own specialized tools and equipment (hardware components). Tesla's system acts like an efficient management structure, assigning the right tasks to the right departments, providing them with the appropriate tools, and coordinating their efforts for optimal productivity and performance.
