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 Dan W Priestley attended the Advanced Clean Transportation (ACT) Expo in Anaheim, California, and provided an update on Tesla’s Semi truck program. The presentation covered several key developments on the status of Tesla’s Nevada Semi Factory, refinements to the Semi, and Tesla’s plans for charging and ramping production through 2026.
Let’s dig in and take a look at everything that was captured by the Out of Spec team at ACT Expo. The original video is embedded below if you’d like to watch it.
Semi Factory & Production Ramp
Priestley reaffirmed the timelines mentioned during Tesla’s Q4 2024 Earnings Call that Tesla will scale Semi production in 2026. To achieve this, Tesla has been actively building and expanding the Gigafactory Nevada site, specifically to support the production of the Tesla Semi. The dedicated Semi facility will have a targeted annual capacity of 50,000 Semi trucks.
Following the beginning of production, Tesla will utilize the initial trucks to integrate into its own logistics operations. This will serve as both a final real-world testing ground as well as an opportunity for Tesla to gather data internally. Tesla plans to begin subsequent customer deliveries throughout 2026 as the ramp-up continues.
Reuters also reported that Tesla is hiring over 1,000 new employees at the Semi Factory to begin the rapid ramping of the program.
Semi has already amassed 7.9 million miles with Tesla’s current testing and operational fleets, providing some real-world data and testing. Feedback for the truck has been exceptionally successful, with many drivers praising the Semi’s performance and comfort.
New Tesla Semi Features
Of course, it wouldn’t be a Tesla keynote without showing off some new things. The Semi will be available in 500-mile and 300-mile range configurations, now featuring updated mirror designs and a drop-down glass section to improve visibility and allow easier interaction with external elements—such as control panels at ports, for example.
New Electric Power Take-Off (e-PTO)
The Tesla Semi will also feature a new capability called Electric Power Take-Off, or e-PTO system. Similar to the PTO systems found on other vehicles, this will allow the Semi’s high-voltage battery to power auxiliary equipment at variable voltages. That includes being able to power things like climate-controlled reefer trailers, potentially replacing the noisy and polluting diesel generators traditionally used for this purpose.
Charging and Batteries
Out of Spec BITS/YouTube
Tesla is also working on an updated battery pack design for the final production design of the Semi. This new pack is designed to be more cost-effective to manufacture. The battery pack itself is slightly smaller than before, but the truck maintains the same level of range through efficiencies. Dan also confirmed during his keynote that the battery cells for the Semi will be sourced domestically inside the United States, helping to alleviate potential burdens due to tariffs.
On the charging front, Tesla is using MCS - the Megawatt Charging System - capable of 1.2MW - and designed specifically for Semi. The system uses the same V4 charging hardware found at Supercharger sites but focuses on that larger power output. Alongside a smaller physical footprint, Tesla will be able to configure these V4 cabinets for either dedicated Semi charging or for shared power scenarios with regular Superchargers. Tesla is also working on an integrated overnight charging product, but Tesla isn’t ready to talk about it yet.
46 Semi Charger Sites Coming
The 46 new MCS sites coming soon.
Out of Spec BITS/YouTube
Finally, Tesla has made substantial investments in a public charging network for the Semi. There are currently 46 sites in progress throughout the United States, and plans for significant expansion throughout 2026 and 2027. These sites are strategically located alongside major truck routes and within industrial areas to support long-haul and regional operations. Tesla is aiming to offer the lowest possible energy costs to operators to help incentivize adoption.
This was one of the best updates to the Tesla Semi we’ve received since its initial unveiling. It seems that the Semi will receive a big portion of Tesla’s attention in 2026, while Robotaxi and FSD Unsupervised take the stage this year.
The Tesla Semi has the potential to transform transportation even more dramatically than EVs already have, serving as a testament to Tesla’s mission to electrify the world.
Sentry Mode is an invaluable tool for owners - capable of keeping the vehicle safe and secure even when you’re not around. This is especially true in recent times, with the misguided and unfortunate incidents surrounding Tesla ownership, including damage to Tesla vehicles, showrooms, and Superchargers.
B-pillar Camera Recording and Dashcam Viewer
With the 2025 Spring Update on 2025.14, Tesla is expanding Sentry Mode’s functionality for certain vehicles with some much-needed changes. Sentry Mode and Dashcam can now record footage from the vehicle’s B-pillar cameras. These cameras are located on the side pillars of the vehicle, between the front and rear doors.
This adds two crucially needed viewpoints, making Tesla’s Sentry Mode a truly 360-degree security system. These cameras also provide the best angles for capturing license plates when parked, so they will be greatly appreciated by owners in the event of an incident.
These vehicles are also receiving an improved Dashcam Viewer, which now displays the six camera feeds along the bottom and a new grid view. It also allows users to jump back or forward in the video in 15-second increments.
However, to the disappointment of many owners, not all vehicles are receiving these updates due to the additional processing power needed.
Limited to Hardware 4 Vehicles, Ryzen Isn’t Enough
We have confirmed that Tesla is only adding the additional camera recording and improved Dashcam Viewer on hardware 4 (HW4 / AI4) vehicles. The newer hardware presumably has the additional processing power and bandwidth needed to handle recording and saving the two additional video streams during Sentry Mode and Dashcam.
For the time being, owners of HW3 vehicles are not receiving this feature. This includes all vehicles with HW3, even those with AMD Ryzen infotainment systems. If you’re not sure whether your vehicle has HW3 or HW4, you can refer to our FSD hardware guide.
While there’s no doubt that recording two additional camera streams would be more computationally intensive, we hope that Tesla adds the improved Dashcam Viewer to HW3 vehicles in a future update.
Cybertruck Also Missing Improved Sentry Mode
Surprisingly, and most confusing for many - is the fact that the Cybertruck is also not receiving the improved Dashcam Viewer and B-pillar camera recording with this update. This struck us as odd, especially since the Cybertruck is currently the only vehicle with the improved, more efficient version of Sentry Mode.
Every Cybertruck is equipped with HW4 and AMD Ryzen infotainment units, so this clearly isn’t a hardware restriction. It’s possible the more efficient Sentry Mode is playing a role here due to the infrastructure changes. However, we expect Tesla to address this in a future update and eventually release these features for the Cybertruck as well.
Given the Cybertruck’s high visibility and its status as a frequent target for both positive and negative attention, many owners hoped that the Cybertruck would be one of the vehicles to receive this feature.
Adaptive Headlights
Tesla finally started rolling out its adaptive headlights in North America. While the new Model Y already came with the feature when it was released last month, other vehicles with matrix headlights are now receiving the feature in the Spring Update.
All vehicles with matrix headlights are receiving this feature, which includes the new and old Model 3, first-gen Model Y, and the new Model S and Model X.
If you’re not sure if your vehicle includes matrix headlights, check out our guide. What’s interesting here is that older vehicles that were retrofitted with matrix headlights due to an accident or user replacement are also receiving the adaptive headlights feature.
Legacy Model S & Model X
As with most updates, the older legacy Model S and Model X are not receiving all the features included in this update. Unfortunately, some of the features, which include the Blind Spot Camera on the instrument cluster, Save Trunk Height Based on Location and Keep Accessory Power On are limited to the new Model S and X.
Legacy S and X models will receive the Alternative Trip Plans feature, Avoid Highways (Requires Intel MCU) and the Keyboard Languages feature.
These vehicles are also receiving all the features in the Minor Updates section except for the visualization showing how far the door is opened, which is exclusive to the Cybertruck. These additions include improved music search results, contact photos in the phone app, automatic connecting to hotspots, the ability to show third-party chargers, view Supercharger amenities, and various improvements to music services.
While many users will be disappointed not to receive the B-pillar camera recording and Dashcam Viewer improvements, it’s important to remember that Tesla typically does a great job at bringing features to older vehicles, at least with the Model 3 and Model Y. If a feature isn’t added, it’s usually due to a hardware limitation.
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