Chapter 1: A Historical Overview of GPUs

The rise of graphics processing units (GPUs) can be traced back to the 1970s and 80s alongside the gaming boom. By the 90s, the demand for enhanced 3D graphics for arcade and console games spurred companies like Nintendo, Sony, and Fujitsu to innovate. However, it was Nvidia's introduction of the GeForce 8 series in the early 2000s that truly transformed GPUs into versatile computing devices, extending their use beyond gaming.

Today, GPUs are integral to various fields, from gaming and linear algebra to image processing and machine learning. A pivotal 2009 study by Andrew Ng and his Stanford colleagues highlighted the potential of GPUs to address computational challenges in machine learning. They noted that modern GPUs significantly outperform multicore CPUs, paving the way for advancements in deep learning methodologies.

As the deep learning sector expanded, companies sought to develop specialized computing units that capitalized on the foundational work of GPUs. In 2016, Google led this initiative with its Tensor Processing Unit (TPU), specifically tailored for neural network training.

With the demand for high-performance hardware surging, numerous chip manufacturers have emerged. Notably, SambaNova, established in 2017, is at the forefront of AI-specific chips, even rivaling Nvidia. Their approach, as described by VentureBeat's Poornima Apte, focuses on "software-driven hardware," prioritizing the specific needs of AI systems. Cerebras, another innovative startup, recently collaborated with OpenAI, aiming to power the next generation of GPT models with what could be "the largest computer chip ever."

Chapter 2: Tesla's In-House Chip Development

In June 2021, Tesla's Andrej Karpathy revealed the company's strategic shift towards fully self-driving vehicles. He detailed their latest neural network training cluster, which comprises 720 nodes, each equipped with 8 Nvidia A100 GPUs, totaling 1.8 EFLOPs at FP16. This impressive configuration places Tesla's cluster among the world's top supercomputers.

While this setup has served Tesla well, the desire to eliminate reliance on external chip manufacturers and address the inefficiencies of Nvidia GPUs for machine learning has led Tesla to pursue in-house chip development. This initiative culminated in Project Dojo.

Chapter 3: Project Dojo — A New Era in AI Supercomputing

Project Dojo aims to "achieve the best AI training performance while enabling larger and more complex neural network models in a power-efficient and cost-effective manner." Unlike traditional supercomputers, Dojo is designed specifically for AI tasks, rather than attempting to compete with the most powerful generic supercomputers.

A major challenge in supercomputer design is balancing increased computing power with high bandwidth and low latency. Tesla's solution is a distributed 2D architecture that integrates robust chips with a unique network fabric, facilitating swift communication.

True to their commitment to vertical integration, Tesla plans to develop every component of Dojo, from the training nodes to the D1 chip, and ultimately, the ExaPOD, which will replace their current GPU stack.

Chapter 4: The ExaPOD — Tesla's Future Supercluster

The ExaPOD is constructed by assembling training tiles into a modular design that enhances bandwidth and minimizes latency. This supercluster achieves 1.1 EFLOPs at BF16, positioning it as a formidable contender against existing GPU clusters.

Elon Musk has indicated that Dojo could be operational as early as next year, with plans for future iterations that promise even greater improvements.