Tesla has developed a novel solution to overcome physical limitations in computational processing, detailed in their patent US20260017019A1. This innovation, dubbed "The Mixed-Precision Bridge," aims to bridge the gap for low-energy, cost-effective 8-bit technology, which traditionally handles only basic integers. The technology also extends to "Rot8 premium technology" for advanced 32-bit processing.
This advancement is expected to unlock the AI5 processor, a chip anticipated to be 40 times more powerful than current hardware. This is particularly significant for the Tesla Optimus robot, which is equipped with a 2.3 kWh battery – approximately one-thirtieth the capacity of a Model 3 battery. Without this new technology, utilizing 32-bit GPU processing would deplete this battery in under four hours and consume over 500W solely for computational tasks.
Necessity is the mother of invention.
The @Tesla_AI team is epicly hardcore. No one can match Tesla’s real-world AI. https://twitter.com/elonmusk/status/2012489863652597918
Consequently, Tesla has managed to reduce the computational power budget to below 100W, effectively solving the "thermal wall" problem. This enables robots to maintain balance and awareness throughout an 8-hour working schedule without overheating.
Engineers at Tesla Incorporate Accuracy into the Reading of Road Signs
The patent introduces "Silicon Bridge," a technology that empowers Optimus and FSD systems with enhanced intelligence. This is achieved without compromising their operational range or causing circuits to overheat. This innovation effectively transforms Tesla's budget hardware into a machine with supercomputer-class capabilities.
Furthermore, the technology addresses a critical issue of AI "forgetting." In previous FSD models, if a stop sign was momentarily obscured by another vehicle for approximately five seconds, the system would "forget" its presence.
Tesla's new approach utilizes a "long-context" window, allowing the AI to access data from as far back as 30 seconds or more. However, standard positional mathematics can lead to drift over extended time intervals.
Tesla's mixed-precision pipeline resolves this by maintaining high positional resolution. This ensures that the AI accurately remembers the precise location of an occluded stop sign, even after significant movement around it. The Tesla team asserts that the Rotational Positional Embedding (RoPE) rotations are sufficiently precise to keep the sign fixed to its 3D coordinate within the car's internal spatial map.
Tesla Achieves Independence from NVIDIA's CUDA Ecosystem
The patent details a specific method for audio processing that employs a Log-Sum-Exp approximation. By operating within the logarithmic domain, this technique can manage a wide "dynamic range" of sound, from faint ambient noise to loud sirens, using only 8-bit processors. This avoids "clipping" loud sounds and losing quieter ones, enabling a vehicle to perceive and differentiate its environment with 32-bit precision.
Tesla implements Quantization-Aware Training (QAT). Instead of training AI in a high-precision 32-bit environment and then reducing its precision, which often leads to degraded performance, Tesla trains its AI from the outset within a simulated 8-bit constrained environment. This approach unlocks the potential for implementing Tesla's AI into significantly smaller devices than a car.
Integrating this mathematical framework directly into the silicon also grants Tesla strategic independence. Tesla is no longer reliant on NVIDIA's CUDA ecosystem and is positioned to adopt a Dual-Foundry Strategy, utilizing both Samsung and TSMC for manufacturing.
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