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TESLAAI HOMEPAGE CAREERSSKIP TO MAIN CONTENT

 1. Telegram
 2. Twitter
 3. Tokenomics
 4. Roadmaps
 5. Whitepapaer
 6. Audit

 1. Profile
 2. US





 



 


TESLAAI & ROBOTICS

TESLAAI is currently the voice of the mainstream consciousness of the
blockchain. Our technical team will make the Teslaai's cryptocurrency market
better according to this demand.
We develop and deploy autonomy at scale in vehicles, robots and more. We believe
that an approach based on advanced AI for vision and planning, supported by
efficient use of inference hardware, is the only way to achieve a general
solution for full self-driving, bi-pedal robotics and beyond.


TESLAAI BOT

Create a general purpose, bi-pedal, autonomous humanoid robot capable of
performing unsafe, repetitive or boring tasks. Achieving that end goal requires
building the software stacks that enable balance, navigation, perception and
interaction with the physical world. We’re hiring deep learning, computer
vision, motion planning, controls, mechanical and general software engineers to
solve some of our hardest engineering challenges.




FSD CHIP

Build AI inference chips to run our Full Self-Driving software, considering
every small architectural and micro-architectural improvement while squeezing
maximum silicon performance-per-watt. Perform floor-planning, timing and power
analyses on the design. Write robust tests and scoreboards to verify
functionality and performance. Implement drivers to program and communicate with
the chip, focusing on performance optimization and redundancy. Finally, validate
the silicon chip and bring it to mass production in our vehicles.




DOJO CHIP

Build AI training chips to power our Dojo system. Implement bleeding-edge
technology from the smallest training nodes to the multi-die training tiles.
Design and architect for maximum performance, throughput and bandwidth at every
granularity. Dictate physical methodology, floor-planning and other physical
aspects of the chip. Develop pre-silicon verification and post-silicon
validation methods to ensure functional correctness. Write compilers and drivers
to optimize power and performance for our neural networks throughout the entire
Dojo system. For more information about Dojo’s arithmetic formats and methods,
download our latest whitepaper.




DOJO SYSTEM

Design and build the Dojo system, from the silicon firmware interfaces to the
high-level software APIs meant to control it. Solve hard problems with
state-of-the-art technology for high-power delivery and cooling, and write
control loops and monitoring software that scales. Work with every aspect of
system design where the limit is only your imagination, employing the full
prowess of our mechanical, thermal and electrical engineering teams to create
the next-generation of machine learning compute for use in Tesla datacenters.
Collaborate with Tesla fleet learning to deploy training workloads using our
massive datasets, and design a public facing API that will bring Dojo to the
masses.




NEURAL NETWORKS

Apply cutting-edge research to train deep neural networks on problems ranging
from perception to control. Our per-camera networks analyze raw images to
perform semantic segmentation, object detection and monocular depth estimation.
Our birds-eye-view networks take video from all cameras to output the road
layout, static infrastructure and 3D objects directly in the top-down view. Our
networks learn from the most complicated and diverse scenarios in the world,
iteratively sourced from our fleet of millions of vehicles in real time. A full
build of Autopilot neural networks involves 48 networks that take 70,000 GPU
hours to train 🔥. Together, they output 1,000 distinct tensors (predictions) at
each timestep.

Replay


AUTONOMY ALGORITHMS

Develop the core algorithms that drive the car by creating a high-fidelity
representation of the world and planning trajectories in that space. In order to
train the neural networks to predict such representations, algorithmically
create accurate and large-scale ground truth data by combining information from
the car's sensors across space and time. Use state-of-the-art techniques to
build a robust planning and decision-making system that operates in complicated
real-world situations under uncertainty. Evaluate your algorithms at the scale
of the entire Tesla fleet.




CODE FOUNDATIONS

Throughput, latency, correctness and determinism are the main metrics we
optimize our code for. Build the Autopilot software foundations up from the
lowest levels of the stack, tightly integrating with our custom hardware.
Implement super-reliable bootloaders with support for over-the-air updates and
bring up customized Linux kernels. Write fast, memory-efficient low-level code
to capture high-frequency, high-volume data from our sensors, and to share it
with multiple consumer processes— without impacting central memory access
latency or starving critical functional code from CPU cycles. Squeeze and
pipeline compute across a variety of hardware processing units, distributed
across multiple system-on-chips.




EVALUATION INFRASTRUCTURE

Build open- and closed-loop, hardware-in-the-loop evaluation tools and
infrastructure at scale, to accelerate the pace of innovation, track performance
improvements and prevent regressions. Leverage anonymized characteristic clips
from our fleet and integrate them into large suites of test cases. Write code
simulating our real-world environment, producing highly realistic graphics and
other sensor data that feed our Autopilot software for live debugging or
automated testing.