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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.
Tesla’s reason for Starlink integration on Cybercab might surprise you, as the company’s Head of AI, Ashok Elluswamy, finally shed some light on the reason they are putting a satellite internet terminal on its ride-hailing-geared vehicle.
On Monday, Tesla officially confirmed that it would integrate Starlink V5 terminals into Cybercab vehicles, something many Tesla fans had figured the company would do, as the vehicle is primarily geared toward giving rides without any passenger intervention.
Starlink V5 directly integrated in Cybercab pic.twitter.com/FxzTtzjB6I
— Tesla (@Tesla) July 20, 2026
The ability to access the internet would allow riders to work or play in the car with their devices. It seemed like a more-than-reasonable feature to add to the Cybercab, which made its way off the production lines for the first time earlier this year.
Tesla reveals first vehicle model to receive Starlink integration
However, the move is not for the rider, as Elluswamy confirmed on Monday night. Instead, it’s actually for Tesla to be able to have a constant connection to the cars in the Robotaxi fleet so it can troubleshoot issues, contact riders, or resolve other issues.
Elluswamy said:
“It is still not required for safe operation of the vehicle. Connectivity is primarily meant for navigation, customer service and, in general, fleet management.”
It is still not required for safe operation of the vehicle. Connectivity is primarily meant for navigation, customer service and, in general, fleet management.
— Ashok Elluswamy (@aelluswamy) July 20, 2026
Many initially assumed the option of constant connectivity would be enabled on the Cybercab for passenger entertainment or work. With the Cybercab, passengers won’t be doing anything but enjoying the ride, so it seemed more than logical that they would be hanging out with Starlink internet access as an amenity.
However, Tesla’s primary concern with Robotaxi is safety, and nailing these first unsupervised rides is a crucial step to setting a good narrative on how effective driverless transportation can be.
Being able to get in touch with passengers or a vehicle if something is wrong is a crucial part of the overall experience, and preventative measures are being taken by Tesla to ensure a smooth process, even in the worst-case.
Tesla has expanded its Robotaxi program in Florida to include two new cities: Tampa and Orlando.
This marks the second and third cities to be added to the company’s available locations for autonomous ride-hailing in the Sunshine State, joining Miami, which was the first Florida city to offer Robotaxi rides.
Tesla announced the addition of Orlando and Tampa to the Robotaxi program on Tuesday morning. The cities now join Austin, Dallas, Houston, Miami, and the San Francisco Bay Area as locations where Tesla can operate its Robotaxi platform:
Robotaxi now in Tampa & Orlando! pic.twitter.com/fYtmXgJq3O
— Tesla Robotaxi (@robotaxi) July 21, 2026
These rides are unsupervised, as AI Head Ashok Elluswamy confirmed the suite in Florida is operating without safety drivers or anyone within the cabin to assist with operation.
Orlando Tesla Robotaxi Operation
The geofence in Orlando covers a prominent irregular shaded zone on the map, roughly 4-6 miles across in key dimensions, so it likely measures somewhere between 25 and 45 square miles, which is comparable to other early Tesla launches in other cities.
It encompasses central and southern areas bounded by major highways including SR-417 and SR-528, including parts of the Orlando metro core, tourism-adjacent zones, and residential/commercial districts. This represents an initial targeted rollout in a tourist-heavy region, positioned for quick expansion via Tesla’s software updates.
Tampa Tesla Robotaxi Operation
In Tampa, the shape of the geofence is a shaded polygon covering key neighborhoods, explicitly including West Tampa, Tampa Heights, Hyde Park, and downtown Tampa proper, with boundaries along major roads and the Hillsborough River area.
This focuses on high-demand central zones and will offer tourists and citygoers rides without drivers.
Robotaxi Progress
Tesla has been operating Robotaxi since last June, when it launched in Austin. The geofences in most regions have already expanded several times since their launch last year, but the bigger complaint is vehicle availability. Tesla has been working to add more Robotaxi-enabled vehicles to its fleet.
The company still plans to utilize its Cybercab, a new vehicle that is being produced at Gigafactory Texas, for the Robotaxi suite alongside the Model Y, which has been the vehicle of choice for Tesla with early operations.
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Tesla’s AI Chief just hinted at something big for FSD v14 lite owners
Tesla’s AI chief suggests the newest FSD v14 Lite build may finally go wide release.
Tesla’s head of AI, Ashok Elluswamy, noted on Sunday that the newest FSD v14 Lite build rolling out to Hardware 3 cars is likely the version that goes to wide release, the strongest signal yet that Tesla is near to closing out an early access phase that Hardware 3 owners have waited more than a year for.
Elluswamy made the comment in response to an extensive review from Tesla owner Zack, known on X as @BLKMDL3, who tested software version 2026.20.6.10 and detailed the changes in a lengthy post. “FSD v14 Lite (for Tesla AI3 hardware vehicles) review.
The update restarts a rollout that had stalled after its initial release. Tesla began pushing FSD v14 Lite to Hardware 3 early access drivers on June 29, bringing driving behavior learned on the newer Hardware 4 computer down to the more limited chip that has powered Tesla vehicles built between 2019 and early 2023. That release, as we covered in detail, gave roughly 4 million HW3 vehicles their first meaningful update since being frozen on version 12.6 in early 2025.
Tesla Full Self-Driving v14 ‘Lite’ Release Notes: new capabilities and features
The latest build adds features that bring Hardware 3 closer in line with what Hardware 4 owners already have. FSD can now start directly from park without a brake pedal confirmation, a change Zack called a small but meaningful quality of life improvement. The interface also picks up the blue “P” park icon, approaching destination alerts, and a dedicated Self-Driving app with streak tracking, all details previously exclusive to the AI4 branch of v14, as outlined in Tesla’s original release notes.
The stakes around Hardware 3 go beyond software polish. Tesla sold the Full Self-Driving package for years on the promise that every vehicle equipped with it had the hardware needed to eventually drive itself without supervision. That promise broke down during Tesla’s Q1 2026 earnings call, when Musk acknowledged HW3 cars could not run unsupervised FSD, prompting Tesla to offer trade-in discounts and hardware retrofits alongside the Lite software track.
Tesla confirmed HW3 can’t do Unsupervised FSD but there’s more to the story
Tesla has continued to frame v14 Lite as the primary path forward for the HW3 fleet, telling owners in April that international markets would follow the U.S. rollout once regulatory approvals came through. For now, HW3 owners in the early access group are the only ones running the new build. A broader rollout would mark the second major software delivery to the legacy fleet since Tesla first released FSD v14 to Hardware 4 vehicles, and the first sign since June that the Lite program is still moving rather than stuck in early access limbo.
Tesla’s reason for Starlink integration on Cybercab might surprise you
Tesla Robotaxi program expands in Florida to two new cities
