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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 Cybercab display highlights interior wizardry in the small two-seater
Photos and videos of the production Cybercab were shared in posts on social media platform X.
The Tesla Cybercab is currently on display at the U.S. Department of Transportation in Washington, D.C., and observations of the production vehicle are highlighting some of its notable design details.
Photos and videos of the production Cybercab were shared in posts on social media platform X.
Observers of the Cybercab display unit noted that the two-seat Robotaxi provides unusually generous legroom for a vehicle of its size. Based on the vehicle’s video, the compact two-seater appears to offer more legroom than Tesla’s larger vehicles such as the Model Y, Model X, and Cybertruck.
The Cybercab’s layout allows Tesla to dedicate nearly the entire cabin to passengers. The vehicle is designed without a steering wheel or pedals, which helps maximize interior space.
Footage from the display also highlights the Cybercab’s large center screen, which is positioned prominently in front of the passenger bench. The display appears intended to provide entertainment and ride information while the vehicle operates autonomously.
Images of the vehicle also show an additional camera integrated into the Cybercab’s C-pillar. The extra camera appears to expand the vehicle’s field of view, which would be useful as Tesla works toward fully unsupervised Full Self-Driving.
Tesla engineers have previously explained that the Cybercab was designed to be highly efficient both in manufacturing and in operation. Cybercab Lead Engineer Eric E. stated in 2024 that the Robotaxi would be built with roughly half the number of parts used in a Model 3 sedan.
“Two seats unlocks a lot of opportunity aerodynamically. It also means we cut the part count of Cybercab down by a substantial margin. We’re gonna be delivering a car that has roughly half the parts of Model 3 today,” the Tesla engineer said.
The Tesla engineer also noted that the Cybercab’s cargo area can accommodate multiple golf bags, two carry-on suitcases, and two full-size checked bags. The trunk can also fit certain bicycles and a foldable wheelchair depending on size, which is quite impressive for a small car like the Cybercab.
Elon Musk
Elon Musk’s xAI wins permit for power plant supporting AI data centers
The development was reported by CNBC, citing confirmation from the Mississippi Department of Environmental Quality (MDEQ).
Mississippi regulators have approved a permit allowing Elon Musk’s artificial intelligence company xAI to construct a natural gas power plant in Southaven. The facility is expected to support the company’s expanding AI infrastructure tied to its Colossus data center operations near Memphis.
The development was reported by CNBC, citing confirmation from the Mississippi Department of Environmental Quality (MDEQ).
According to the report, regulators “voted to approve the permit” of xAI subsidiary MZX Tech LLC to construct a power plant featuring 41 natural gas-burning turbines “after careful consideration of all public comments and community concerns.”
The Mississippi Department of Environmental Quality stated that the permit followed a regulatory review process that included public comments and community input. Jaricus Whitlock, air division chief for the MDEQ, stated that the project met all applicable environmental standards.
“The proposed PSD permit in front of the board today not only meets all state and federal permitting regulations, but goes above and beyond what is required by law. MDEQ and the EPA agree that not a single person around our facilities will be exposed to unhealthy levels of air pollution,” Whitlock stated.
The planned facility will help provide electricity for xAI’s AI computing infrastructure in the Memphis region.
The Southaven project forms part of xAI’s efforts to scale computing capacity for its artificial intelligence systems.
The company currently operates two major data centers in Memphis, known as Colossus 1 and Colossus 2, which provide computing power for xAI’s Grok AI models. xAI is also planning to build another large data center in Southaven called Macrohardrr, which would be located in a warehouse previously used by GXO Logistics.
Large-scale AI training requires substantial computing power and electricity, prompting technology companies to develop dedicated energy infrastructure for their data centers.
SpaceX President Gwynne Shotwell previously stated that xAI plans to develop 1.2 gigawatts of power capacity for its Memphis-area AI supercomputer site as part of the federal government’s Ratepayer Protection Pledge. The commitment was announced during an event with United States President Donald Trump.
“As part of today’s commitment, we will take extensive additional steps to continue to reduce the costs of electricity for our neighbors. xAI will therefore commit to develop 1.2 GW of power as our supercomputer’s primary power source. That will be for every additional data center as well. We will expand what is already the largest global Megapack power installation in the world,” Shotwell said.
“The installation will provide enough backup power to power the city of Memphis, and more than sufficient energy to power the town of Southaven, Mississippi where the data center resides. We will build new substations and invest in electrical infrastructure to provide stability to the area’s grid.”
Elon Musk
Tesla China teases Optimus robot’s human-looking next-gen hands
The image was shared by Tesla AI’s account on Weibo and later reposted by Tesla community members on X.
A new teaser shared by Tesla’s China team appears to show a pair of unusually human-like hands for Optimus.
The image was shared by Tesla AI’s account on Weibo and later reposted by Tesla community members on X.
As could be seen in the teaser image, the new version of Optimus’ hands features proportions and finger structures that look strikingly similar to those of a human hand. Their appearance suggests that they might have dexterity approaching that of a human hand.
If the image reflects a new generation of Optimus’ hands, it could indicate Tesla is continuing to refine one of the most critical components of its humanoid robot.
Hands are widely viewed as one of the most difficult engineering challenges in robotics. For Optimus to perform complex real-world work, from manufacturing tasks to household activities, its hands would need to be the best in the industry.
Elon Musk has repeatedly described Optimus as Tesla’s most important long-term product. In posts on social media platform X, Musk has stated that Optimus could eventually become the first real-world Von Neumann machine.
In theory, a Von Neumann machine is a self-replicating system capable of building copies of itself using available materials. The concept was originally proposed by mathematician John von Neumann in the mid-20th century.
“Optimus will be the first Von Neumann machine, capable of building civilization by itself on any viable planet,” Musk wrote in a post on X.
If Optimus is expected to carry out complex work autonomously in the future, high levels of dexterity will likely be essential. This makes the development of advanced robotic hands a key step towards Musk’s long-term expectations for the product.
Tesla Cybercab display highlights interior wizardry in the small two-seater
Elon Musk’s xAI wins permit for power plant supporting AI data centers
