How Tesla’s “Universal Translator” Will Streamline FSD for Any Platform

November 20, 2024

By Karan Singh

Not a Tesla App

It’s time for another dive into how Tesla intends to implement FSD. Once again, a shout out to SETI Park over on X for their excellent coverage of Tesla’s patents.

This time, it's about how Tesla is building a “universal translator” for AI, allowing its FSD or other neural networks to adapt seamlessly to different hardware platforms.

That translating layer can allow a complex neural net—like FSD—to run on pretty much any platform that meets its minimum requirements. This will drastically help reduce training time, adapt to platform-specific constraints, decide faster, and learn faster.

We’ll break down the key points of the patents and make them as understandable as possible. This new patent is likely how Tesla will implement FSD on non-Tesla vehicles, Optimus, and other devices.

Decision Making

Imagine a neural network as a decision-making machine. But building one also requires making a series of decisions about its structure and data processing methods. Think of it like choosing the right ingredients and cooking techniques for a complex recipe. These choices, called "decision points," play a crucial role in how well the neural network performs on a given hardware platform.

To make these decisions automatically, Tesla has developed a system that acts like a "run-while-training" neural net. This ingenious system analyzes the hardware's capabilities and adapts the neural network on the fly, ensuring optimal performance regardless of the platform.

Constraints

Every hardware platform has its limitations – processing power, memory capacity, supported instructions, and so on. These limitations act as "constraints" that dictate how the neural network can be configured. Think of it like trying to bake a cake in a kitchen with a small oven and limited counter space. You need to adjust your recipe and techniques to fit the constraints of your kitchen or tools.

Tesla's system automatically identifies these constraints, ensuring the neural network can operate within the boundaries of the hardware. This means FSD could potentially be transferred from one vehicle to another and adapt quickly to the new environment.

Let’s break down some of the key decision points and constraints involved:

Data Layout: Neural networks process vast amounts of data. How this data is organized in memory (the "data layout") significantly impacts performance. Different hardware platforms may favor different layouts. For example, some might be more efficient with data organized in the NCHW format (batch, channels, height, width), while others might prefer NHWC (batch, height, width, channels). Tesla's system automatically selects the optimal layout for the target hardware.
Algorithm Selection: Many algorithms can be used for operations within a neural network, such as convolution, which is essential for image processing. Some algorithms, like the Winograd convolution, are faster but may require specific hardware support. Others, like Fast Fourier Transform (FFT) convolution, are more versatile but might be slower. Tesla's system intelligently chooses the best algorithm based on the hardware's capabilities.
Hardware Acceleration: Modern hardware often includes specialized processors designed to accelerate neural network operations. These include Graphics Processing Units (GPUs) and Tensor Processing Units (TPUs). Tesla's system identifies and utilizes these accelerators, maximizing performance on the given platform.

Satisfiability

To find the best configuration for a given platform, Tesla employs a "satisfiability solver." This powerful tool, specifically a Satisfiability Modulo Theories (SMT) solver, acts like a sophisticated puzzle-solving engine. It takes the neural network's requirements and the hardware's limitations, expressed as logical formulas, and searches for a solution that satisfies all constraints. Try thinking of it as putting the puzzle pieces together after the borders (constraints) have been established.

Here's how it works, step-by-step:

Define the Problem: The system translates the neural network's needs and the hardware's constraints into a set of logical statements. For example, "the data layout must be NHWC" or "the convolution algorithm must be supported by the GPU."
Search for Solutions: The SMT solver explores the vast space of possible configurations, using logical deduction to eliminate invalid options. It systematically tries different combinations of settings, like adjusting the data layout, selecting algorithms, and enabling hardware acceleration.
Find Valid Configurations: The solver identifies configurations that satisfy all the constraints. These are potential solutions to the "puzzle" of running the neural network efficiently on the given hardware.

Optimization

Finding a working configuration is one thing, but finding the best configuration is the real challenge. This involves optimizing for various performance metrics, such as:

Inference Speed: How quickly the network processes data and makes decisions. This is crucial for real-time applications like FSD.
Power Consumption: The amount of energy used by the network. Optimizing power consumption is essential for extending battery life in electric vehicles and robots.
Memory Usage: The amount of memory required to store the network and its data. Minimizing memory usage is especially important for resource-constrained devices.
Accuracy: Ensuring the network maintains or improves its accuracy on the new platform is paramount for safety and reliability.

Tesla's system evaluates candidate configurations based on these metrics, selecting the one that delivers the best overall performance.

Translation Layer vs Satisfiability Solver

It's important to distinguish between the "translation layer" and the satisfiability solver. The translation layer is the overarching system that manages the entire adaptation process. It includes components that analyze the hardware, define the constraints, and invoke the SMT solver. The solver is a specific tool used by the translation layer to find valid configurations. Think of the translation layer as the conductor of an orchestra and the SMT solver as one of the instruments playing a crucial role in the symphony of AI adaptation.

Simple Terms

Imagine you have a complex recipe (the neural network) and want to cook it in different kitchens (hardware platforms). Some kitchens have a gas stove, others electric; some have a large oven, others a small one. Tesla's system acts like a master chef, adjusting the recipe and techniques to work best in each kitchen, ensuring a delicious meal (efficient AI) no matter the cooking environment.

What Does This Mean?

Now, let’s wrap this all up and put it into context—what does it mean for Tesla? There’s quite a lot, in fact. It means that Tesla is building a translation layer that will be able to adapt FSD for any platform, as long as it meets the minimum constraints.

That means Tesla will be able to rapidly accelerate the deployment of FSD on new platforms while also finding the ideal configurations to maximize both decision-making speed and power efficiency across that range of platforms.

Putting it all together, Tesla is preparing to license FSD, Which is an exciting future. And not just on vehicles - remember that Tesla’s humanoid robot - Optimus - also runs on FSD. FSD itself may be an extremely adaptable vision-based AI.

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Tesla's New Model Y to Receive Adaptive Headlight Support in U.S. Soon

February 10, 2025

By Karan Singh

@DriveGreen80167 on X

In the latest episode of Jay Leno’s Garage, Tesla’s VP of Vehicle Engineering, Lars Moravy, confirmed that the new Model Y will feature adaptive headlights.

As Moravy was talking about the updated headlights in the vehicle, which now sit a few inches lower than before, he stated that in a couple of months, Tesla will add adaptive headlights in the U.S.

While Tesla has already introduced adaptive headlights in Europe and the Indo-Pacific, the feature has yet to make its way to North America.

Originally delayed in the U.S. due to regulatory issues, manufacturers have been able to implement adaptive headlights since mid-2024. Meanwhile, competitors like Rivian and Mercedes-Benz have already rolled out their own full matrix headlight systems, matching what’s available in other regions.

Update: This article has been updated to clarify that adaptive headlights will indeed be launched in the U.S., shortly after the vehicle launching in March.

Adaptive Headlights

Back in October 2024, Lars confirmed that matrix headlight functionality was just around the corner for North America. However, as we enter 2025, it’s still unclear when Teslas with matrix headlights will receive the feature.

Currently, Tesla in North America supports adaptive high beams and automatic headlight adjustment for curves, but full matrix functionality has yet to be rolled out. Meanwhile, matrix headlights are already available in Europe, where they selectively dim individual beam pixels to reduce glare for oncoming traffic and adapt to curves in the road.

It was surprising that matrix functionality wasn’t included in the comprehensive 2024 Tesla Holiday Update. This feature would likely improve safety ratings, so we can only assume Tesla is diligently working to secure regulatory approval.

Adaptive Headlights on Other Models

Lars didn’t confirm whether the refreshed Model Y comes with the same headlights as the new Model 3 and the Cybertruck, instead simply calling them "matrix-style” headlights.

The headlights on the new Model Y appear very similar to those available in the 2024+ Model 3, possibly meaning these other models will also receive adaptive headlight capabilities in the next couple of months.

For vehicles with older-style matrix headlights, it’s unlikely that adaptive beams support will launch at the same time, but they will hopefully become available soon afterward.

You can check our guide here to see if your vehicle includes matrix headlights.

Tesla Starts Underwriting Its Own Insurance: Will They Insure Their Own Robotaxis?

February 10, 2025

By Karan Singh

Not a Tesla App

For the first time since launching Tesla Insurance in 2019, Tesla will begin underwriting its own policies, starting in California.

Tesla Insurance originally debuted in California and has since expanded to several U.S. states. Until now, policies were underwritten by State National, a subsidiary of the Markel Insurance Group. However, Tesla is now transitioning to fully in-house underwriting, beginning with its home state.

As part of this shift, California Tesla Insurance customers who receive an in-app offer to switch will be eligible for a one-time 3% discount on their next term’s premium—covered entirely by Tesla Insurance.

What is Underwriting

Underwriting is the process an insurance company uses to assess risk and determine whether to offer coverage, at what price, and under what terms.

Insurers evaluate factors such as driving history, credit score, age, vehicle type, and location. In Tesla’s case, vehicle driving data (not available in California) also plays a key role in risk assessment. These factors help classify drivers into risk categories, which influence their base premium.

From there, coverage limits, deductibles, and policy inclusions or exclusions can further adjust the final premium up or down.

Robotaxi and Other Benefits

At first glance, underwriting insurance might seem like a complex and costly process for Tesla. However, there are several compelling reasons why this move makes sense.

Insurance Income: Insurance is a highly profitable industry. Companies set rates based on risk, offering lower premiums to safer drivers and higher rates to riskier ones. This not only maximizes profitability but also incentivizes safer driving behavior, reducing overall claims.

Data Advantage: Tesla collects vast amounts of driving data through its Safety Score system. While California doesn’t allow Safety Score to impact premiums, Tesla can still use this data in the underwriting process to refine risk assessments and pricing for its vehicles.

Control Over Repair Costs: By underwriting its own policies, Tesla gains direct control over repairs and total loss decisions. This allows them to dictate when, where, and how repairs are done, optimizing costs for parts, labor, and service while ensuring vehicles are fixed according to Tesla’s standards.

FSD-Driven Discounts: Tesla has already begun offering insurance discounts for drivers using Full Self-Driving (FSD). By underwriting its own policies, Tesla could expand these incentives, potentially offering greater discounts to frequent FSD users in the future.

Preparing for Robotaxi: Perhaps the biggest long-term reason for this shift is the June launch of the Robotaxi fleet. How will Tesla insure these vehicles? The answer is simple—by underwriting its own policies and assuming liability.

Tesla’s decision to underwrite its own insurance isn’t just about cutting out middlemen—it’s a step toward lowering costs, increasing profitability, and preparing for the future of autonomous driving, a risk many insurance companies may be unwilling to make.

Further Expansion

This could be a strong sign that Tesla is preparing to expand its insurance offerings now that it has taken on the underwriting process itself. In July 2024, Tesla hired a former GEICO insurance executive to lead the expansion of Tesla Insurance and help reduce costs—a move that now appears to be paying off.

Rather than a traditional expansion, Tesla has instead made a bold move by bringing underwriting in-house, something few expected. However, it aligns with Tesla’s strategy of vertically integrating and controlling key aspects of its business, whether in manufacturing, software, or now, insurance.

If this pilot program proves successful, it could pave the way for Tesla Insurance to launch in more states—and potentially even other countries. With 2025 shaping up to be a pivotal year, we may see Tesla accelerate its insurance expansion sooner than expected.