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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 is not sparing any expense in ensuring the Cybercab is safe
Images shared by the longtime watcher showed 16 Cybercab prototypes parked near Giga Texas’ dedicated crash test facility.
The Tesla Cybercab could very well be the safest taxi on the road when it is released and deployed for public use. This was, at least, hinted at by the intensive safety tests that Tesla seems to be putting the autonomous two-seater through at its Giga Texas crash test facility.
Intensive crash tests
As per recent images from longtime Giga Texas watcher and drone operator Joe Tegtmeyer, Tesla seems to be very busy crash testing Cybercab units. Images shared by the longtime watcher showed 16 Cybercab prototypes parked near Giga Texas’ dedicated crash test facility just before the holidays.
Tegtmeyer’s aerial photos showed the prototypes clustered outside the factory’s testing building. Some uncovered Cybercabs showed notable damage and one even had its airbags engaged. With Cybercab production expected to start in about 130 days, it appears that Tesla is very busy ensuring that its autonomous two-seater ends up becoming the safest taxi on public roads.
Prioritizing safety
With no human driver controls, the Cybercab demands exceptional active and passive safety systems to protect occupants in any scenario. Considering Tesla’s reputation, it is then understandable that the company seems to be sparing no expense in ensuring that the Cybercab is as safe as possible.
Tesla’s focus on safety was recently highlighted when the Cybertruck achieved a Top Safety Pick+ rating from the Insurance Institute for Highway Safety (IIHS). This was a notable victory for the Cybertruck as critics have long claimed that the vehicle will be one of, if not the, most unsafe truck on the road due to its appearance. The vehicle’s Top Safety Pick+ rating, if any, simply proved that Tesla never neglects to make its cars as safe as possible, and that definitely includes the Cybercab.
Elon Musk
Tesla’s Elon Musk gives timeframe for FSD’s release in UAE
Provided that Musk’s timeframe proves accurate, FSD would be able to start saturating the Middle East, starting with the UAE, next year.
Tesla CEO Elon Musk stated on Monday that Full Self-Driving (Supervised) could launch in the United Arab Emirates (UAE) as soon as January 2026.
Provided that Musk’s timeframe proves accurate, FSD would be able to start saturating the Middle East, starting with the UAE, next year.
Musk’s estimate
In a post on X, UAE-based political analyst Ahmed Sharif Al Amiri asked Musk when FSD would arrive in the country, quoting an earlier post where the CEO encouraged users to try out FSD for themselves. Musk responded directly to the analyst’s inquiry.
“Hopefully, next month,” Musk wrote. The exchange attracted a lot of attention, with numerous X users sharing their excitement at the idea of FSD being brought to a new country. FSD (Supervised), after all, would likely allow hands-off highway driving, urban navigation, and parking under driver oversight in traffic-heavy cities such as Dubai and Abu Dhabi.
Musk’s comments about FSD’s arrival in the UAE were posted following his visit to the Middle Eastern country. Over the weekend, images were shared online of Musk meeting with UAE Defense Minister, Deputy Prime Minister, and Dubai Crown Prince HH Sheikh Hamdan bin Mohammed. Musk also posted a supportive message about the country, posting “UAE rocks!” on X.
FSD recognition
FSD has been getting quite a lot of support from foreign media outlets. FSD (Supervised) earned high marks from Germany’s largest car magazine, Auto Bild, during a test in Berlin’s challenging urban environment. The demonstration highlighted the system’s ability to handle dense traffic, construction sites, pedestrian crossings, and narrow streets with smooth, confident decision-making.
Journalist Robin Hornig was particularly struck by FSD’s superior perception and tireless attention, stating: “Tesla FSD Supervised sees more than I do. It doesn’t get distracted and never gets tired. I like to think I’m a good driver, but I can’t match this system’s all-around vision. It’s at its best when both work together: my experience and the Tesla’s constant attention.” Only one intervention was needed when the system misread a route, showcasing its maturity while relying on vision-only sensors and over-the-air learning.
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Tesla quietly flexes FSD’s reliability amid Waymo blackout in San Francisco
“Tesla Robotaxis were unaffected by the SF power outage,” Musk wrote in his post.
Tesla highlighted its Full Self-Driving (Supervised) system’s robustness this week by sharing dashcam footage of a vehicle in FSD navigating pitch-black San Francisco streets during the city’s widespread power outage.
While Waymo’s robotaxis stalled and caused traffic jams, Tesla’s vision-only approach kept operating seamlessly without remote intervention. Elon Musk amplified the clip, highlighting the contrast between the two systems.
Tesla FSD handles total darkness
The @Tesla_AI account posted a video from a Model Y operating on FSD during San Francisco’s blackout. As could be seen in the video, streetlights, traffic signals, and surrounding illumination were completely out, but the vehicle drove confidently and cautiously, just like a proficient human driver.
Musk reposted the clip, adding context to reports of Waymo vehicles struggling in the same conditions. “Tesla Robotaxis were unaffected by the SF power outage,” Musk wrote in his post.
Musk and the Tesla AI team’s posts highlight the idea that FSD operates a lot like any experienced human driver. Since the system does not rely on a variety of sensors and a complicated symphony of factors, vehicles could technically navigate challenging circumstances as they emerge. This definitely seemed to be the case in San Francisco.
Waymo’s blackout struggles
Waymo faced scrutiny after multiple self-driving Jaguar I-PACE taxis stopped functioning during the blackout, blocking lanes, causing traffic jams, and requiring manual retrieval. Videos shared during the power outage showed fleets of Waymo vehicles just stopping in the middle of the road, seemingly confused about what to do when the lights go out.
In a comment, Waymo stated that its vehicles treat nonfunctional signals as four-way stops, but “the sheer scale of the outage led to instances where vehicles remained stationary longer than usual to confirm the state of the affected intersections. This contributed to traffic friction during the height of the congestion.”
A company spokesperson also shared some thoughts about the incidents. “Yesterday’s power outage was a widespread event that caused gridlock across San Francisco, with non-functioning traffic signals and transit disruptions. While the failure of the utility infrastructure was significant, we are committed to ensuring our technology adjusts to traffic flow during such events,” the Waymo spokesperson stated, adding that it is “focused on rapidly integrating the lessons learned from this event, and are committed to earning and maintaining the trust of the communities we serve every day.”
Tesla is not sparing any expense in ensuring the Cybercab is safe
Tesla’s Elon Musk gives timeframe for FSD’s release in UAE
