Home AI News Unlocking the Mysteries of Tesla's Dojo AI Supercomputer

Unlocking the Mysteries of Tesla's Dojo AI Supercomputer

Introduction
The Importance of Chip Startups and Innovation in the Semiconductor Industry
Tesla's Investment in Chip Design for Autonomous Driving
The Brain and Algorithm in Self-Driving Vehicles
Training and Inference in Self-Driving Algorithms
The Limitations of Training Neural Networks
The Need for Custom Hardware: Introducing Tesla Dojo
The Design of Tesla Dojo D1 Chip
The Dojo Training Tiles: Scaling Up the System
The Dojo Supercomputer: A Network of Tiles
Comparing Tesla Dojo to Other AI Chips in the Market
Tesla's Approach to Self-Driving Network Development
The Challenges of Data Collection and Bias in Self-Driving Algorithms
Conclusion

Introduction

In recent years, the semiconductor industry has witnessed a surge in chip startups and innovation. This has led to the development of new ideas and technologies in chip design, fueled by venture capital investments and significant funding. One prominent player in this field is Tesla, the electric vehicle manufacturer. As a leader in the autonomous driving space, Tesla recognized the need for custom hardware to support its self-driving algorithms. The result is Tesla Dojo, a cutting-edge machine learning computer designed specifically for autonomous driving. In this article, we will explore the architecture and design of Tesla Dojo, its capabilities, and its role in Tesla's self-driving network.

The Importance of Chip Startups and Innovation in the Semiconductor Industry

The semiconductor industry has experienced a Wave of chip startups and innovation in recent years. These startups, backed by venture capital and substantial funding, have brought forth new ideas and technologies that are reshaping the industry. With a focus on specific verticals and tailored chip designs, these startups are disrupting traditional approaches and challenging established players.

One of the significant advantages of chip startups is their ability to Create custom designs that cater to the unique needs of specific industries. Rather than relying on existing solutions in the market, these startups can develop chips that optimize performance, power efficiency, and cost-effectiveness for their intended applications. This level of customization and specialization is driving rapid advancements and reshaping various industries, including electric vehicles.

Tesla's Investment in Chip Design for Autonomous Driving

Tesla, as a leading electric vehicle manufacturer, has recognized the importance of chip design in driving innovation and enhancing its autonomous driving capabilities. Instead of relying on off-the-shelf chips, Tesla has invested significant resources into developing its own machine learning computers, specifically designed for autonomous driving.

By developing its own chips, Tesla can create hardware that aligns perfectly with its self-driving algorithms. This synergy between software and hardware allows for optimized performance and efficiency, resulting in more accurate and reliable autonomous driving capabilities.

The Brain and Algorithm in Self-Driving Vehicles

Self-driving vehicles rely on two critical components: the brain and the algorithm. While the brain refers to the hardware system used for processing and making decisions, the algorithm is the software that enables the vehicle to navigate and respond to its environment.

The algorithm in self-driving vehicles needs to be trained and optimized offline in a data center using vast amounts of labeled data. This training process involves using machine learning techniques to predict the best course of action Based on the available data. Once trained, the algorithm can be used in real-time applications where it infers results from new data.

Training and Inference in Self-Driving Algorithms

Training a self-driving algorithm is a long and complex process that requires substantial amounts of computational power and vast datasets. The algorithm needs to process millions of hours of video and billions of hours of compute to train effectively. The output of the training process is an algorithm with the right numbers and calculations to produce accurate results.

Inference, on the other HAND, refers to the use of a trained algorithm with new data in real-time applications. Once the algorithm is trained, it can be deployed in self-driving vehicles to make Instant decisions based on the input it receives. Inference allows the vehicle to navigate its environment autonomously and respond to changing conditions.

The Limitations of Training Neural Networks

Training neural networks for autonomous driving comes with several limitations. Broad data, software, time, and power constraints limit the scalability and speed of the training process. While increasing computational resources can speed up training, there comes a point where additional resources result in diminishing returns.

Tesla faced this challenge, and CEO Elon Musk acknowledged that the cost and time requirements of training their self-driving algorithms using available GPUs were astronomical. To address this issue, Tesla ventured into developing custom hardware that could overcome these limitations and provide the necessary computational power for training neural networks.

The Need for Custom Hardware: Introducing Tesla Dojo

To meet the demands of training its self-driving algorithms, Tesla embarked on developing its custom hardware known as Tesla Dojo. Dojo is a machine learning computer specifically designed for training neural networks for autonomous driving.

Dojo consists of multiple components, starting with the Dojo D1 chip. This chip is a massive 645 square millimeter silicon die built on TSMC's seven-nanometer process. It contains 354 processing cores, each with its dedicated interface and local SRAM. The D1 chip is designed to handle the computational workload required for training neural networks efficiently.

The Design of Tesla Dojo D1 Chip

The Tesla Dojo D1 chip is a unique and powerful piece of hardware with multiple components. It features 354 cores per chip, each with its dedicated local SRAM and knock router for communication with other cores on the chip. The chip has a custom instruction set architecture and specialized instructions tailored for machine learning tasks.

The cores on the D1 chip are designed to be lightweight yet powerful, with limited protection mechanisms to prevent interference between Threads. The chip uses a four-way simultaneous multi-threading approach, allowing multiple threads to run compute operations simultaneously while managing data flow efficiently.

Data storage on the D1 chip is handled by the 1.25 megabytes of SRAM per Core. This SRAM acts as the primary data storage medium, replacing traditional cache structures found in other chips. The SRAM provides high-speed access to data, with a latency of around four to six cycles. Additionally, the D1 chip features an efficient on-chip network that enables fast communication between cores and facilitates data transfer.

The Dojo Training Tiles: Scaling Up the System

To Scale up the computational power of Tesla Dojo, the D1 chips are arranged in what Tesla calls "training tiles." Each training tile consists of 25 D1 chips arranged in a 5x5 GRID, with a total of 8,850 processing cores and 11 gigabytes of SRAM. The training tiles are powered by a unique packaging solution, making efficient use of power and thermal management.

Training multiple neural networks simultaneously requires efficient communication between tiles. Each tile is equipped with five Dojo interface processors (DIPs) that manage data communication within the tile and between neighboring tiles. The DIPs use custom low-power 30s links developed by Tesla, enabling high-bandwidth data transfer within the tile and high-speed connectivity between tiles.

The Dojo Supercomputer: A Network of Tiles

The scalability of Tesla Dojo goes beyond individual tiles. Multiple tiles can be connected to create a larger network of Dojo supercomputers. A full-scale Dojo supercomputer consists of 120 tiles arranged in a 3x40 array, delivering a total compute power of 1 exaFLOP of bf16 machine learning performance.

To facilitate communication and data transfer between tiles, Tesla has developed a custom networking switch and networking protocol called z-plane topology. This innovative approach minimizes latency, optimizes bandwidth usage, and reduces congestion within the Dojo system. It allows data to be transmitted across multiple routes, balancing latency and bandwidth for efficient communication.