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Tesla files Parallel Processing patent to reduce FSD hardware error risks

Credit: Tesla

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Tesla has filed a new patent for “Parallel Processing System Runtime State Reload,” comprising of a system of three or more processors working in conjunction to effectively eliminate the possibility of hardware failure during the use of Autopilot or Full Self-Driving. The patent outlines a robust system of parallel processors that can operate in the event that one of them fails or experiences a runtime state error. “Should one of the parallel processors fail, at least one other processor would be available to continue performing autonomous driving functions,” the patent shows.

The patent was filed and published on August 26th and comes just a week after the company’s Artificial Intelligence Day event that was held last Thursday. Outlining a system of at least three processors operating in parallel, it is monitored by circuitry and can locate and identify if one of the three parallel-operating processors is having a runtime state error. The circuitry will then identify a second processor to switch to in the event of a runtime error, access the runtime state of the second processor, and load the runtime state of the second, operational processor into the first processor, which is experiencing a runtime error.

(Credit: Tesla)

Tesla describes the patent in detail:

“A system on a Chip (SoC) includes a plurality of processing systems arranged on a single integrated circuit. Each of these separate processing systems typically performs a corresponding set of processing functions. The separate processing systems typically interconnect via one or more communication bus structures that include an N-bit wide data bus (N, an integer greater than one). Some SoCs are deployed within systems that require high availability, e.g., financial processing systems, autonomous driving systems, medical processing systems, and air traffic control systems, among others. These parallel processing systems typically operate upon the same input data and include substantially identical processing components, e.g., pipeline structure, so that each of the parallel processing systems, when correctly operating, produces substantially the same output. Thus, should one of the parallel processors fail, at least one other processor would be available to continue performing autonomous driving functions.”

Technically speaking, the autonomous vehicle needs only one processor to function as described in an accurate fashion. However, these processors can be overloaded with data when loading into the Neural Network and could experience short-term and non-permanent operational errors. When this occurs, the system would then switch to one of the other processors for normal operation, with at least two backup processors in this patent, as it repeatedly mentions a series of three.

Tesla details its self-driving Supercomputer that will bring in the Dojo era

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The second processor would then activate and load the runtime state into the first processor to make the primary processor chip operational once again:

“Thus, in order to overcome the above-described shortcomings, among other shortcomings, a parallel processing system of an embodiment of the present disclosure includes at least three processors operating in parallel, state monitoring circuitry, and state reload circuitry. The state monitoring circuitry couples to the at least three parallel processors and is configured to monitor runtime states of the at least three parallel processors and identify a first processor of the at least three parallel processors having at least one runtime state error. The state reload circuitry couples to the at least three parallel processors and is configured to select a second processor of the at least three parallel processors for state reload, access a runtime state of the second processor, and load the runtime state of the second processor into the first processor.”

The purpose of this patent is to continue system availability, even when the primary processor is experiencing functionality issues due to overuse. The two additional processors essentially act as “backup” and can determine whether autonomous driving systems are meant to be enabled if the first processor experiences an error. “With one particular example of this aspect, the parallel processing system supports autonomous driving and the respective sub-systems of the at least three parallel processors are safety sub-systems that determine whether autonomous driving is to be enabled.”

FIG. 13 is a timing diagram illustrating clocks of the circuits of FIGS. 8 and 10 according to one or more other described embodiments. As shown, the runtime state (data1) of first processor/first sub-system is determined to have at least one error. In response to this determination by the state monitoring/state reload circuitry, the signal st_reload1 is asserted to initiate the loading of runtime state (data2) from second processor/second sub-system into the first processor/first sub-system. With the embodiment of FIG. 13, a first clock (clk1) is used for the first processor/first sub-system and a second clock (clk1) is used for the second processor/second sub-system. There exists a positive skew between the first clock (clk1) and the second clock (clk2), resulting in a late cycle of the loading of the runtime state (data2) of the second processor/second sub-system into the first processor/sub-system, potentially resulting in errors in the runtime state reload process. (Credit: U.S. Patent Office)

It also appears that this patent aligns with Tesla CEO Elon Musk’s previous description of the Dojo self-driving Supercomputer, which was detailed at AI Day. To increase the accuracy and encourage the parallel operation of the processors, the system will utilize a clock input to calibrate the two processors, increasing the accuracy of the system.

Tesla has focused on accurate FSD operation and has revised its strategy on several occasions. After moving to a camera-only approach earlier this year for the Model 3 and Model Y, the company is experiencing more accurate FSD operation through the harmonized processing of its eight exterior cameras. The operation of internal processors, which are responsible for compiling, compressing, and sending data to the Neural Network, can fail temporarily, so the presence of backup processors to continue comprehending self-driving data is a positive idea.

The full patent is available below:

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Tesla Patent Parallel Processing System Runtime State Reload by Joey Klender on Scribd

Joey has been a journalist covering electric mobility at TESLARATI since August 2019. In his spare time, Joey is playing golf, watching MMA, or cheering on any of his favorite sports teams, including the Baltimore Ravens and Orioles, Miami Heat, Washington Capitals, and Penn State Nittany Lions. You can get in touch with joey at joey@teslarati.com. He is also on X @KlenderJoey. If you're looking for great Tesla accessories, check out shop.teslarati.com

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Tesla’s northernmost Supercharger in North America opens

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Credit: Tesla

Tesla has opened its northernmost Supercharger in Fairbanks, Alaska, with eight V4 stalls located in one of the most frigid cities in the U.S.

Located just 196 miles from the Arctic Circle, Fairbanks’s average temperature for the week was around -12 degrees Fahrenheit. However, there are plenty of Tesla owners in Alaska who have been waiting for more charging options out in public.

There are only 36 total Supercharger stalls in Alaska, despite being the largest state in the U.S.

Eight Superchargers were added to Fairbanks, which will eventually be a 48-stall station. Tesla announced its activation today:

The base price per kWh is $0.43 at the Fairbanks Supercharger. Thanks to its V4 capabilities, it can charge at speeds up to 325 kW.

Despite being the northernmost Supercharger in North America, it is not even in the Top 5 northernmost Superchargers globally, because Alaska is south of Norway. The northernmost Supercharger is in Honningsvåg, Norway. All of the Top 5 are in the Scandanavian country.

Tesla’s Supercharger expansion in 2025 has been impressive, and although it experienced some early-quarter slowdowns due to V3-to-V4 hardware transitions, it has been the company’s strongest year for deployments.

Through the three quarters of 2025, the company has added 7,753 stations and 73,817 stalls across the world, a 16 percent increase in stations and an 18 percent increase in stalls compared to last year.

Tesla is on track to add over 12,000 stalls for the full year, achieving an average of one new stall every hour, an impressive statistic.

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Recently, the company wrapped up construction at its Supercharger Oasis in Lost Hills, California, a 168-stall Supercharger that Tesla Solar Panels completely power. It is the largest Supercharger in the world.

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Tesla hints toward Premium Robotaxi offering with Model S testing

Why Tesla has chosen to use a couple of Model S units must have a reason; the company is calculated in its engineering and data collection efforts, so this is definitely more than “we just felt like giving our drivers a change of scenery.”

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Credit: Sawyer Merritt | X

Tesla Model S vehicles were spotted performing validation testing with LiDAR rigs in California today, a pretty big switch-up compared to what we are used to seeing on the roads.

Tesla utilizes the Model Y crossover for its Robotaxi fleet. It is adequately sized, the most popular vehicle in its lineup, and is suitable for a wide variety of applications. It provides enough luxury for a single rider, but enough room for several passengers, if needed.

However, the testing has seemingly expanded to one of Tesla’s premium flagship offerings, as the Model S was spotted with the validation equipment that is seen entirely with Model Y vehicles. We have written several articles on Robotaxi testing mules being spotted across the United States, but this is a first:

Why Tesla has chosen to use a couple of Model S units must have a reason; the company is calculated in its engineering and data collection efforts, so this is definitely more than “we just felt like giving our drivers a change of scenery.”

It seems to hint that Tesla could add a premium, more luxury offering to its Robotaxi platform eventually. Think about it: Uber has Uber Black, Lyft has Lyft Black. These vehicles and services are associated with a more premium cost as they combine luxury models with more catered transportation options.

Tesla could be testing the waters here, and it could be thinking of adding the Model S to its fleet of ride-hailing vehicles.

Reluctant to remove the Model S from its production plans completely despite its low volume contributions to the overall mission of transitioning the world to sustainable energy, the flagship sedan has always meant something. CEO Elon Musk referred to it, along with its sibling Model X, as continuing on production lines due to “sentimental reasons.”

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However, its purpose might have been expanded to justify keeping it around, and why not? It is a cozy, premium offering, and it would be great for those who want a little more luxury and are willing to pay a few extra dollars.

Of course, none of this is even close to confirmed. However, it is reasonable to speculate that the Model S could be a potential addition to the Robotaxi fleet. It’s capable of all the same things the Model Y is, but with more luxuriousness, and it could be the perfect addition to the futuristic fleet.

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Rivian unveils self-driving chip and autonomy plans to compete with Tesla

Rivian, a mainstay in the world of electric vehicle startups, said it plans to roll out an Autonomy+ subscription and one-time purchase program, priced at $49.99 per month and $2,500 up front, respectively, for access to its self-driving suite.

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Credit: Rivian

Rivian unveiled its self-driving chip and autonomy plans to compete with Tesla and others at its AI and Autonomy Day on Thursday in Palo Alto, California.

Rivian, a mainstay in the world of electric vehicle startups, said it plans to roll out an Autonomy+ subscription and one-time purchase program, priced at $49.99 per month and $2,500 up front, respectively, for access to its self-driving suite.

CEO RJ Scaringe said it will learn and become more confident and robust as more miles are driven and it gathers more data. This is what Tesla uses through a neural network, as it uses deep learning to improve with every mile traveled.

He said:

“I couldn’t be more excited for the work our teams are driving in autonomy and AI. Our updated hardware platform, which includes our in-house 1600 sparse TOPS inference chip, will enable us to achieve dramatic progress in self-driving to ultimately deliver on our goal of delivering L4. This represents an inflection point for the ownership experience – ultimately being able to give customers their time back when in the car.”

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At first, Rivian plans to offer the service to personally-owned vehicles, and not operate as a ride-hailing service. However, ride-sharing is in the plans for the future, he said:

“While our initial focus will be on personally owned vehicles, which today represent a vast majority of the miles to the United States, this also enables us to pursue opportunities in the rideshare space.”

The Hardware

Rivian is not using a vision-only approach as Tesla does, and instead will rely on 11 cameras, five radar sensors, and a single LiDAR that will face forward.

It is also developing a chip in-house, which will be manufactured by TSMC, a supplier of Tesla’s as well. The chip will be known as RAP1 and will be about 50 times as powerful as the chip that is currently in Rivian vehicles. It will also do more than 800 trillion calculations every second.

RAP1 powers the Autonomy Compute Module 3, known as ACM3, which is Rivian’s third-generation autonomy computer.

ACM3 specs include:

  • 1600 sparse INT8 TOPS (Trillion Operations Per Second).
  • The processing power of 5 billion pixels per second.
  • RAP1 features RivLink, a low-latency interconnect technology allowing chips to be connected to multiply processing power, making it inherently extensible.
  • RAP1 is enabled by an in-house developed AI compiler and platform software

As far as LiDAR, Rivian plans to use it in forthcoming R2 cars to enable SAE Level 4 automated driving, which would allow people to sit in the back and, according to the agency’s ratings, “will not require you to take over driving.”

More Details

Rivian said it will also roll out advancements to the second-generation R1 vehicles in the near term with the addition of UHF, or Universal Hands-Free, which will be available on over 3.5 million miles of roadway in the U.S. and Canada.

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Rivian will now join the competitive ranks with Tesla, Waymo, Zoox, and others, who are all in the race for autonomy.

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