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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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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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Tesla partners with Lemonade for new insurance program

Tesla recently was offered “almost free” coverage for Full Self-Driving by Lemonade’s Shai Wininger, President and Co-founder, who said it would be “happy to explore insuring Tesla FSD miles for (almost) free.”

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Credit: Tesla

Tesla owners in California, Oregon, and Arizona can now use Lemonade Insurance, the firm that recently said it could cover Full Self-Driving miles for “almost free.”

Lemonade, which offered the new service through its app, has three distinct advantages, it says:

  • Direct Connection for no telematics device needed
  • Better customer service
  • Smarter pricing

The company is known for offering unique, fee-based insurance rates through AI, and instead of keeping unclaimed premiums, it offers coverage through a flat free upfront. The leftover funds are donated to charities by its policyholders.

On Thursday, it announced that cars in three states would be able to be connected directly to the car through its smartphone app, enabling easier access to insurance factors through telematics:

Tesla recently was offered “almost free” coverage for Full Self-Driving by Lemonade’s Shai Wininger, President and Co-founder, who said it would be “happy to explore insuring Tesla FSD miles for (almost) free.”

The strategy would be one of the most unique, as it would provide Tesla drivers with stable, accurate, and consistent insurance rates, while also incentivizing owners to utilize Full Self-Driving for their travel miles.

Tesla Full Self-Driving gets an offer to be insured for ‘almost free’

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This would make FSD more cost-effective for owners and contribute to the company’s data collection efforts.

Data also backs Tesla Full Self-Driving’s advantages as a safety net for drivers. Recent figures indicate it was nine times less likely to be in an accident compared to the national average, registering an accident every 6.36 million miles. The NHTSA says a crash occurs approximately every 702,000 miles.

Tesla also offers its own in-house insurance program, which is currently offered in twelve states so far. The company is attempting to enter more areas of the U.S., with recent filings indicating the company wants to enter Florida and offer insurance to drivers in that state.

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Tesla Model Y gets hefty discounts and more in final sales push

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Credit: Tesla

Tesla Model Y configurations are getting hefty discounts and more benefits as the company is in the phase of its final sales push for the year.

Tesla is offering up to $1,500 off new Model Y Standard trims that are available in inventory in the United States. Additionally, Tesla is giving up to $2,000 off the Premium trims of the Model Y. There is also one free upgrade included, such as a paint color or interior color, at no additional charge.

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Tesla is hoping to bolster a relatively strong performance through the first three quarters of the year, with over 1.2 million cars delivered through the first three quarters.

This is about four percent under what the company reported through the same time period last year, as it was about 75,000 vehicles ahead in 2024.

However, Q3 was the company’s best quarterly performance of all time, and it surged because of the loss of the $7,500 EV tax credit, which was eliminated in September. The imminent removal of the credit led to many buyers flocking to Tesla showrooms to take advantage of the discount, which led to a strong quarter for the company.

2024 was the first year in the 2020s when Tesla did not experience a year-over-year delivery growth, as it saw a 1 percent slide from 2023. The previous years saw huge growth, with the biggest coming from 2020 to 2021, when Tesla had an 87 percent delivery growth.

This year, it is expected to be a second consecutive slide, with a drop of potentially 8 percent, if it manages to deliver 1.65 million cars, which is where Grok projects the automaker to end up.

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Tesla will likely return to its annual growth rate in the coming years, but the focus is becoming less about delivery figures and more about autonomy, a major contributor to the company’s valuation. As AI continues to become more refined, Tesla will apply these principles to its Full Self-Driving efforts, as well as the Optimus humanoid robot project.

Will Tesla thrive without the EV tax credit? Five reasons why they might

These discounts should help incentivize some buyers to pull the trigger on a vehicle before the year ends. It will also be interesting to see if the adjusted EV tax credit rules, which allowed deliveries to occur after the September 30 cutoff date, along with these discounts, will have a positive impact.

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