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Tesla Dojo is a supercomputer designed and built by Tesla for computer vision video processing and recognition. [1] It is used for training Tesla's machine learning models to improve its Full Self-Driving (FSD) advanced driver-assistance system .
Tesla's Dojo supercomputer consists of several "system trays" of the company’s in-house D1 chips, which are built into cabinets that then merge into an "ExaPOD" supercomputer.
Dojo will be used to label the data Tesla receives from the vehicles with cameras that Tesla has on the road. If a user allows, Tesla can pull video data from thousands of cars and use it for ...
This time, Tesla developers wanted to remove virtually all of the 300,000-plus lines of code in v11 and replace it with AI that can continuously learn and improve with each mile a Tesla car drives.
Tesla Dojo is a supercomputer designed from the ground up by Tesla for computer vision video processing and recognition. It will be used to train Tesla's machine learning models to improve FSD. Dojo was first mentioned by Musk in April 2019 [164] [165] and August 2020. [165] It was officially announced by Musk at Tesla's AI Day on August 19 ...
In January 2024, Tesla announced a $500 million project to build a Dojo supercomputer cluster at the factory despite Musk's characterizing Dojo as a "long shot" for AI success. At the same time, the company was investing greater amounts in computer hardware made by others to support its AI training programs for its Full Self Driving and Optimus ...
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A floating-point variable can represent a wider range of numbers than a fixed-point variable of the same bit width at the cost of precision. A signed 32-bit integer variable has a maximum value of 2 31 − 1 = 2,147,483,647, whereas an IEEE 754 32-bit base-2 floating-point variable has a maximum value of (2 − 2 −23 ) × 2 127 ≈ 3.4028235 ...