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Tesla’s Neural Network adaptability to hardware highlighted in new patent application

(Credit: Tesla Driver/YouTube)

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Tesla’s developments in the artificial intelligence arena are one of the most important aspects of its current and future technology, and this includes adapting neural networks to various hardware platforms. A recent patent publication titled “System and Method for Adapting a Neural Network Model On a Hardware Platform” provides a bit of insight into how the electric car maker is taking on the challenge.

In general, a neural network is a set of algorithms designed to gather data and recognize patterns from it. The particular data being collected depends on the platform involved and what kind of information it can send to the network, i.e., cameras/image data, etc. Differences between platforms mean differences in the neural network algorithms, and adapting them is something time consuming for developers. Just as apps have to be programmed to work based on the operating system or hardware on a phone or tablet, for example, so too do neural networks. Tesla’s answer to the adaptation issue is automation (of course).

During the adaptation process of a neural network to specific hardware, decisions must be made by a software developer based on available options built into the hardware being used. Each of these options, in turn, usually requires research, hardware documentation review, and impact analysis, with each set of options chosen, eventually adding up to a configuration for the neural network to use. Tesla’s application calls these options “decision points,” and they are a vital part of how their invention functions.

Credit: Tesla/USPTO

According to the application, after plugging in a neural network model and the specific hardware platform information for adaptation, software code traverses the network to learn where the decision points are, then runs the hardware parameters against those points to provide available configurations. More specifically, the software method looks at the hardware constraints (such as processing resources and performance metrics) and generates setups for the neural network that will satisfy the requirements for it to operate correctly. From the application:

In order to produce a concrete implementation of an abstract neural network, a number of implementation decisions about one or more of system’s data layout, numerical precision, algorithm selection, data padding, accelerator use, stride, and more may be made. These decisions may be made on a per-layer or per-tensor basis, so there can potentially be hundreds of decisions, or more, to make for a particular network. Embodiments of the invention take many factors into account before implementing the neural network because many configurations are not supported by underlying software or hardware platforms, and such configurations will result in an inoperable implementation.

Credit: Tesla/USPTO

Tesla’s invention also provides the ability to display the neural network configuration information on a graphical interface to make assessment and selection a bit more user friendly. For instance, different configurations could have different evaluation times, power consumption, or memory consumption. Perhaps an analogy for this process would be selecting configurations based on differences between Track Mode and Range Mode but instead for how you’d want your AI to work with your hardware.

This patent application looks to be one of the products of Tesla’s reported acquisition of DeepScale, an AI startup focused on Full Self Driving and designing neural networks for small devices. The listed inventor, Dr. Michael Driscoll, was a Senior Staff Engineer for DeepScale before transitioning to a Senior Software Engineer position at Tesla. Prior CEO of DeepScale, Dr. Forrest Iandola, also transitioned to Tesla as a Senior Staff Machine Learning Scientist before moving on to independent research this year.

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Accidental computer geek, fascinated by most history and the multiplanetary future on its way. Quite keen on the democratization of space. | It's pronounced day-sha, but I answer to almost any variation thereof.

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Tesla removes Safety Monitors, begins fully autonomous Robotaxi testing

This development, in terms of the Robotaxi program, is massive. Tesla has been working incredibly hard to expand its fleet of Robotaxi vehicles to accommodate the considerable demand it has experienced for the platform.

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Credit: @Mandablorian | X

Tesla has started Robotaxi testing in Austin, Texas, without any vehicle occupants, the company’s CEO Elon Musk confirmed on Sunday. Two Tesla Model Y Robotaxi units were spotted in Austin traveling on public roads with nobody in the car.

The testing phase begins just a week after Musk confirmed that Tesla would be removing Safety Monitors from its vehicles “within the next three weeks.” Tesla has been working to initiate driverless rides by the end of the year since the Robotaxi fleet was launched back in June.

Two units were spotted, with the first being seen from the side and clearly showing no human beings inside the cabin of the Model Y Robotaxi:

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Another unit, which is the same color but was confirmed as a different vehicle, was spotted just a few moments later:

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The two units are traveling in the general vicinity of the South Congress and Dawson neighborhoods of downtown Austin. These are located on the southside of the city.

This development, in terms of the Robotaxi program, is massive. Tesla has been working incredibly hard to expand its fleet of Robotaxi vehicles to accommodate the considerable demand it has experienced for the platform.

However, the main focus of the Robotaxi program since its launch in the Summer was to remove Safety Monitors and initiate completely driverless rides. This effort is close to becoming a reality, and the efforts of the company are coming to fruition.

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It is a drastic step in the company’s trek for self-driving technology, as it plans to expand it to passenger vehicles in the coming years. Tesla owners have plenty of experience with the Full Self-Driving suite, which is not fully autonomous, but is consistently ranked among the best-performing platforms in the world.

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Tesla refines Full Self-Driving, latest update impresses where it last came up short

We were able to go out and test it pretty extensively on Saturday, and the changes Tesla made from the previous version were incredibly impressive, especially considering it seemed to excel where it last came up short.

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Credit: TESLARATI

Tesla released Full Self-Driving v14.2.1.25 on Friday night to Early Access Program (EAP) members. It came as a surprise, as it was paired with the release of the Holiday Update.

We were able to go out and test it pretty extensively on Saturday, and the changes Tesla made from the previous version were incredibly impressive, especially considering it seemed to excel where it last came up short.

Tesla supplements Holiday Update by sneaking in new Full Self-Driving version

With Tesla Full Self-Driving v14.2.1, there were some serious regressions. Speed Profiles were overtinkered with, causing some modes to behave in a strange manner. Hurry Mode was the most evident, as it refused to go more than 10 MPH over the speed limit on freeways.

It would routinely hold up traffic at this speed, and flipping it into Mad Max mode was sort of over the top. Hurry is what I use most frequently, and it had become somewhat unusable with v14.2.1.

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It seemed as if Speed Profiles should be more associated with both passing and lane-changing frequency. Capping speeds does not help as it can impede the flow of traffic. When FSD travels at the speed of other traffic, it is much more effective and less disruptive.

With v14.2.1.25, there were three noticeable changes that improved its performance significantly: Speed Profile refinements, lane change confidence, and Speed Limit recognition.

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Speed Profile Refinement

Speed Profiles have been significantly improved. Hurry Mode is no longer capped at 10 MPH over the speed limit and now travels with the flow of traffic. This is much more comfortable during highway operation, and I was not required to intervene at any point.

With v14.2.1, I was sometimes assisting it with lane changes, and felt it was in the wrong place at the wrong time more frequently than ever before.

However, this was one of the best-performing FSD versions in recent memory, and I really did not have any complaints on the highway. Speed, maneuvering, lane switching, routing, and aggressiveness were all perfect.

Lane Changes

v14.2.1 had a tendency to be a little more timid when changing lanes, which was sort of frustrating at times. When the car decides to change lanes and turn on its signal, it needs to pull the trigger and change lanes.

It also changed lanes at extremely unnecessary times, which was a real frustration.

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There were no issues today on v14.2.1.25; lane changes were super confident, executed at the correct time, and in the correct fashion. It made good decisions on when to get into the right lane when proceeding toward its exit.

It was one of the first times in a while that I did not feel as if I needed to nudge it to change lanes. I was very impressed.

Speed Limit Recognition

So, this is a complex issue. With v14.2.1, there were many times when it would see a Speed Limit sign that was not meant for the car (one catered for tractor trailers, for example) or even a route sign, and it would incorrectly adjust the speed. It did this on the highway several times, mistaking a Route 30 sign for a 30 MPH sign, then beginning to decelerate from 55 MPH to 30 MPH on the highway.

This required an intervention. I also had an issue leaving a drive-thru Christmas lights display, where the owners of the private property had a 15 MPH sign posted nearly every 200 yards for about a mile and a half.

The car identified it as a 55 MPH sign and sped up significantly. This caused an intervention, and I had to drive manually.

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It seems like FSD v14.2.1.25 is now less reliant on the signage (maybe because it was incorrectly labeling it) and more reliant on map data or the behavior of nearby traffic.

A good example was on the highway today: despite the car reading that Route 30 sign and the Speed Limit sign on the center screen reading 30 MPH, the car did not decelerate. It continued at the same speed, but I’m not sure if that’s because of traffic or map data:

A Lone Complaint

Tesla has said future updates will include parking improvements, and I’m really anxious for them, because parking is not great. I’ve had some real issues with it over the past couple of months.

Today was no different:

Full Self-Driving v14.2.1.25 is really a massive improvement over past versions, and it seems apparent that Tesla took its time with fixing the bugs, especially with highway operation on v14.2.1.

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Tesla hints at Starlink integration with recent patent

“By employing polymer blends, some examples enable RF transmission from all the modules to satellites and other communication devices both inside and outside the vehicle.”

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Credit: Grok

Tesla hinted at a potential Starlink internet terminal integration within its vehicles in a recent patent, which describes a vehicle roof assembly with integrated radio frequency (RF) transparency.

The patent, which is Pub. No U.S. 2025/0368267 describes a new vehicle roof that is made of RF-transparent polymer materials, allowing and “facilitating clear communication with external devices and satellites.”

Tesla believes that a new vehicle roof design, comprised of different materials than the standard metallic or glass elements used in cars today, would allow the company to integrate modern vehicular technologies, “particularly those requiring radio frequency transmission and reception.

Instead of glass or metallic materials, Tesla says vehicles may benefit from high-strength polymer blends, such as Polycarbonate, Acrylonitrile Butadiene Styrene, or Acrylonitrile Styrene Acrylate.

These materials still provide ideal strength metrics for crashworthiness, stiffness for noise, vibration, and harshness control, and are compliant with head impact regulations.

They would also enable better performance with modern technologies, like internet terminals, which need an uninterrupted signal to satellites for maximum reception. Tesla writes in the patent:

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“By employing polymer blends, some examples enable RF transmission from all the modules to satellites and other communication devices both inside and outside the vehicle.”

One of the challenges Tesla seems to be aware of with this type of roof design is the fact that it will still have to enable safety and keep that at the forefront of the design. As you can see in the illustration above, Tesla plans to use four layers to increase safety and rigidity, while also combating noise and vibration.

It notes in the patent that disclosed examples still meet the safety requirements outlined in the Federal Motor Vehicle Safety Standards (FMVSS).

Starlink integrated directly into Tesla vehicles would be a considerable advantage for owners. It would come with a handful of distinct advantages.

Initially, the inclusion of Starlink would completely eliminate cellular dead zones, something that is an issue, especially in rural areas. Starlink would provide connectivity in these remote regions and would ensure uninterrupted service during road trips and off-grid adventures.

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It could also be a critical addition for Robotaxi, as it is crucial to have solid and reliable connectivity for remote monitoring and fleet management.

Starlink’s growing constellation, thanks to SpaceX’s routine and frequent launch schedule, will provide secure, stable, and reliable internet connectivity for Tesla vehicles.

SpaceX reaches incredible milestone with Starlink program

Although many owners have already mounted Starlink Mini dishes under their glass roofs for a similar experience, it may be integrated directly into Teslas in the coming years, either as an upgrade or a standard feature.

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