Tesla Unveils Dojo, Its Trailblazing AI Supercomputer Poised to Revolutionize the Machine Learning Landscape
Tesla recently offered a glimpse into its expanding artificial intelligence infrastructure by unveiling Dojo, an astoundingly powerful supercomputer custom-built to train the automaker‘s proprietary neural networks. Destined to become one of the world‘s fastest AI training systems upon completion in 2022, Dojo signifies Tesla‘s relentless pursuit of autonomous driving dominance through sheer computing force.
The Dawn of the Exascale AI Training Era
To comprehend the magnitude of Dojo‘s computing might, some context on benchmark classifications is warranted. Supercomputers are rated by floating point operations per second (FLOPS), which measures programmable calculation speed. Recently, computing breached the exascale barrier indicating over 1 exaFLOPS – that‘s a mind-boggling quintillion (1018) operations per second!
To illustrate, among the world’s current fastest existing supercomputers like Japan’s Fugaku at 442 petaFLOPS (1 peta = 1000 tera), even running flat out since the dawn of civilization, it would have only just attained one exaFLOPS total. This threshold ushers in a new epoch for simulations, modeling, and artificial intelligence.
Specifically, Dojo’s designed AI training capacity exceeding 1.1 exaFLOPS outguns professional rigs from tech titans like Google, Facebook, OpenAI and more. In fact, it nearly matches the uppermost threshold of AI computing power across organizations in 2021 according to analysis from OpenAI.
Clearly, Tesla is staging its own private revolution in AI infrastructure completely customized from the silicon up just for autonomous driving objectives. The automaker revealed Dojo incorporates unified data center scale architecture with networking, storage, and software integrated on custom chips.
Tesla’s Target of Achieving Full Autonomy Hinges on Dojo’s Exponential Power
Dojo’s specialty lies in supervised training for computer vision, specifically to bring Tesla’s Full Self-Driving (FSD) package to completion relying entirely on cameras without other sensors like radars or lidars. Essentially, Tesla wants to mimic human-level visual understanding of the driving environment using deep neural networks.
For context – Nvidia’s specialized automotive-grade Orin drive computer delivers 200 teraOPS (200 trillion operations per second) while operating autopilot features dependent on sensor fusion. In comparison, Dojo‘s 1.1 exaOPS packs 5000x times more performance at a power budget under 900 kW. This massive parallel processing capacity will be instrumental in actualizing fully autonomous driving.
To construct robust neural networks that can handle the chaotic complexity of real roads, Tesla’s fleet of nearly 1.5 million vehicles record image frames during driving which are then labeled using Dojo‘s proprietary technology. Considering approximately 1 billion miles are driven monthly by Tesla vehicles producing continuous image data, the sheer quantity is unthinkable. Dojo will ingest this visual data avalanche to generate extremely high-fidelity models mapping raw sensory input to driving decisions. These models are continuously refined and updated to the fleet for practical enhancement through a virtuous cycle.
Essentially, Tesla is reinventing the entire self-driving stack from the ground up on the basis of visual scene recognition alone while dedicating ridiculous firepower specifically for this purpose. When fully operational next year, some experts predict Dojo could become the world‘s fastest AI training computer, even surpassing dedicated rigs operated by big tech giants who routinely publish benchmark results. Clearly, Tesla is gearing up to usher in a seismic shift towards vision-based autonomous mobility.
Dojo‘s Architectural Superiority for AI Workloads
Rather than relying on graphical processing units (GPUs) from the likes of Nvidia to shoulder AI training, Tesla’s Dojo incorporates bespoke tensor processing units (TPU) designed in-house explicitly for neural network workloads. TPUs streamline chip architecture to directly match the computation needed, drastically increasing efficiency over repurposed GPUs. For example, Google’s TPU chips deliver up to 30x higher performance per watt than contemporary GPUs.
Reports indicate Dojo chips utilize 7nm manufacturing technology packing over 50 billion transistors each while piecing together elements like ARM cores for programmability. Specifications per chip remain undisclosed thus far. However using 25 Dojo chips unified as one system, the cumulative 45 EFLOPS performance claimed eclipses Nvidia’s new H100 data center GPU with 6 EFLOPS per chip.
Additionally, Dojo utilizes immersion cooling, submerging components in dielectric fluid, enabling high-density installation without overheating issues. Dojo’s backend interconnected scaling to thousands of chips can drive training times drastically lower. Essentially, Tesla has engineered every aspect synergizing hardware and software just for their autonomous purpose.
The Road Ahead – Dojo‘s Far-Reaching Implications
With Dojo emphasized as critical infrastructure for Tesla’s vision-based Full Self Driving system to operate sans lidars or radars, the promise of truly autonomous cars inches closer to reality. On the alternative route, Tesla also monetizes its self-driving technology currently as a subscription package called FSD beta, with Dojo playing an integral role in future feature enhancement.
Here’s where things get interesting – the expansive applicability of Dojo beyond cars to other industries. According to Musk, Dojo‘s cost could be amortized by offering it as a service for training other companies‘ AI models, essentially functioning as a commercial cloud platform for artificial intelligence.
For perspective, Amazon Web Services (AWS) accounted for over 60% of Amazon’s operating income, demonstrating the bountiful profits in cloud computing services. Even partially mirroring that success would deliver substantial financial upside for Tesla’s AI branch. Musk suggested companies developing algorithms for say industrial robots could harness Dojo‘s interface to rapidly iterate designs.
Essentially, Dojo‘s specialized architecture grants unmatched efficiency and scalability for the ultra-intensive math driving today‘s artificial intelligence boom. Beyond pioneering self-driving cars, Tesla could incubate an ecosystem of AI innovation across robotics, smart cities, finance, medicine and more. If Tesla maintains its astounding lead in AI hardware, we may witness the rise of an all-encompassing AI mega-platform with roots in autonomous driving.