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Tesla’s Neural Network adaptability to hardware highlighted in new patent application
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.
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.”
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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Tesla FSD (Supervised) fleet passes 8.4 billion cumulative miles
The figure appears on Tesla’s official safety page, which tracks performance data for FSD (Supervised) and other safety technologies.
Tesla’s Full Self-Driving (Supervised) system has now surpassed 8.4 billion cumulative miles.
The figure appears on Tesla’s official safety page, which tracks performance data for FSD (Supervised) and other safety technologies.
Tesla has long emphasized that large-scale real-world data is central to improving its neural network-based approach to autonomy. Each mile driven with FSD (Supervised) engaged contributes additional edge cases and scenario training for the system.
The milestone also brings Tesla closer to a benchmark previously outlined by CEO Elon Musk. Musk has stated that roughly 10 billion miles of training data may be needed to achieve safe unsupervised self-driving at scale, citing the “long tail” of rare but complex driving situations that must be learned through experience.
The growth curve of FSD Supervised’s cumulative miles over the past five years has been notable.
As noted in data shared by Tesla watcher Sawyer Merritt, annual FSD (Supervised) miles have increased from roughly 6 million in 2021 to 80 million in 2022, 670 million in 2023, 2.25 billion in 2024, and 4.25 billion in 2025. In just the first 50 days of 2026, Tesla owners logged another 1 billion miles.
At the current pace, the fleet is trending towards hitting about 10 billion FSD Supervised miles this year. The increase has been driven by Tesla’s growing vehicle fleet, periodic free trials, and expanding Robotaxi operations, among others.
With the fleet now past 8.4 billion cumulative miles, Tesla’s supervised system is approaching that threshold, even as regulatory approval for fully unsupervised deployment remains subject to further validation and oversight.
Elon Musk
Elon Musk fires back after Wikipedia co-founder claims neutrality and dubs Grokipedia “ridiculous”
Musk’s response to Wales’ comments, which were posted on social media platform X, was short and direct: “Famous last words.”
Elon Musk fired back at Wikipedia co-founder Jimmy Wales after the longtime online encyclopedia leader dismissed xAI’s new AI-powered alternative, Grokipedia, as a “ridiculous” idea that is bound to fail.
Musk’s response to Wales’ comments, which were posted on social media platform X, was short and direct: “Famous last words.”
Wales made the comments while answering questions about Wikipedia’s neutrality. According to Wales, Wikipedia prides itself on neutrality.
“One of our core values at Wikipedia is neutrality. A neutral point of view is non-negotiable. It’s in the community, unquestioned… The idea that we’ve become somehow ‘Wokepidea’ is just not true,” Wales said.
When asked about potential competition from Grokipedia, Wales downplayed the situation. “There is no competition. I don’t know if anyone uses Grokipedia. I think it is a ridiculous idea that will never work,” Wales wrote.
After Grokipedia went live, Larry Sanger, also a co-founder of Wikipedia, wrote on X that his initial impression of the AI-powered Wikipedia alternative was “very OK.”
“My initial impression, looking at my own article and poking around here and there, is that Grokipedia is very OK. The jury’s still out as to whether it’s actually better than Wikipedia. But at this point I would have to say ‘maybe!’” Sanger stated.
Musk responded to Sanger’s assessment by saying it was “accurate.” In a separate post, he added that even in its V0.1 form, Grokipedia was already better than Wikipedia.
During a past appearance on the Tucker Carlson Show, Sanger argued that Wikipedia has drifted from its original vision, citing concerns about how its “Reliable sources/Perennial sources” framework categorizes publications by perceived credibility. As per Sanger, Wikipedia’s “Reliable sources/Perennial sources” list leans heavily left, with conservative publications getting effectively blacklisted in favor of their more liberal counterparts.
As of writing, Grokipedia has reportedly surpassed 80% of English Wikipedia’s article count.
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Tesla Sweden appeals after grid company refuses to restore existing Supercharger due to union strike
The charging site was previously functioning before it was temporarily disconnected in April last year for electrical safety reasons.
Tesla Sweden is seeking regulatory intervention after a Swedish power grid company refused to reconnect an already operational Supercharger station in Åre due to ongoing union sympathy actions.
The charging site was previously functioning before it was temporarily disconnected in April last year for electrical safety reasons. A temporary construction power cabinet supplying the station had fallen over, described by Tesla as occurring “under unclear circumstances.” The power was then cut at the request of Tesla’s installation contractor to allow safe repair work.
While the safety issue was resolved, the station has not been brought back online. Stefan Sedin, CEO of Jämtkraft elnät, told Dagens Arbete (DA) that power will not be restored to the existing Supercharger station as long as the electric vehicle maker’s union issues are ongoing.
“One of our installers noticed that the construction power had been backed up and was on the ground. We asked Tesla to fix the system, and their installation company in turn asked us to cut the power so that they could do the work safely.
“When everything was restored, the question arose: ‘Wait a minute, can we reconnect the station to the electricity grid? Or what does the notice actually say?’ We consulted with our employer organization, who were clear that as long as sympathy measures are in place, we cannot reconnect this facility,” Sedin said.
The union’s sympathy actions, which began in March 2024, apply to work involving “planning, preparation, new connections, grid expansion, service, maintenance and repairs” of Tesla’s charging infrastructure in Sweden.
Tesla Sweden has argued that reconnecting an existing facility is not equivalent to establishing a new grid connection. In a filing to the Swedish Energy Market Inspectorate, the company stated that reconnecting the installation “is therefore not covered by the sympathy measures and cannot therefore constitute a reason for not reconnecting the facility to the electricity grid.”
Sedin, for his part, noted that Tesla’s issue with the Supercharger is quite unique. And while Jämtkraft elnät itself has no issue with Tesla, its actions are based on the unions’ sympathy measures against the electric vehicle maker.
“This is absolutely the first time that I have been involved in matters relating to union conflicts or sympathy measures. That is why we have relied entirely on the assessment of our employer organization. This is not something that we have made any decisions about ourselves at all.
“It is not that Jämtkraft elnät has a conflict with Tesla, but our actions are based on these sympathy measures. Should it turn out that we have made an incorrect assessment, we will correct ourselves. It is no more difficult than that for us,” the executive said.
Tesla FSD (Supervised) fleet passes 8.4 billion cumulative miles
Elon Musk fires back after Wikipedia co-founder claims neutrality and dubs Grokipedia “ridiculous”
