Tesla’s ‘Mixed-Precision Bridge’ cuts AI power under 100W while preserving 32-bit precision

Tesla’s AI team patented a “Mixed-Precision Bridge” (US20260017019A1) that enables low-power 8-bit hardware to perform precise 32-bit operations, reducing compute power for robots and in-car AI below 100W. The approach—branded in the patent as ‘Silicon Bridge’ and a ‘Math Translator’—lets Tesla run RoPE (rotary positional encoding) rotations and long-context windows (keeping spatial data across ~30+ seconds) without positional drift. That fixes prior “forgetting” issues (for example, occluded stop signs being lost after several seconds). Tesla uses Log-Sum-Exp approximations to keep audio dynamic range in the logarithmic domain and applies Quantization-Aware Training (QAT) so models are trained under 8-bit constraints from day one rather than post-training quantization. The patent positions Tesla to deploy more efficient hardware (AI5 processor claimed ~40× current performance) in power-constrained devices like Optimus (2.3 kWh battery) and FSD systems, while reducing dependency on NVIDIA’s CUDA ecosystem and enabling multi-foundry manufacturing with Samsung and TSMC. The company claims the thermal “wall” is solved, allowing 8-hour operation under 100W. Critics note higher R&D and tooling costs for proprietary silicon and compilers despite lower per-unit chip costs. Key keywords: Tesla AI, mixed-precision, 8-bit quantization, Quantization-Aware Training, RoPE rotations, long-context memory, Optimus, FSD, silicon independence.