Lifeboat Foundation

In its second-quarter earnings report for 2023, Tesla revealed its ambitious plan to address vehicle autonomy at scale with four key technology pillars: an extensive real-world dataset, neural net training, vehicle hardware, and vehicle software. Notably, the electric vehicle manufacturer asserted its commitment to developing each of these pillars in-house. A significant milestone in this endeavor was announced as Tesla started the production of its custom-built Dojo training computer, a critical component in achieving faster and more cost-effective neural net training.

While Tesla already possesses one of the world’s most potent Nvidia GPU-based supercomputers, the Dojo supercomputer takes a different approach by utilizing chips specifically designed by Tesla. Back in 2019, Tesla CEO Elon Musk christened this project as “Dojo,” envisioning it as an exceptionally powerful training computer. He claimed that Dojo would be capable of performing an exaflop, or one quintillion (10¹⁸) floating-point operations per second, an astounding level of computational power. To put it into perspective, performing one calculation every second on a one exaFLOP computer system would take over 31 billion years, as reported by Network World.

The development of Dojo has been a continuous process. At Tesla’s AI Day in 2021, the automaker showcased its initial chip and training tiles, which would eventually form a complete Dojo cluster, also known as an “exapod.” Tesla’s plan involves combining two sets of three tiles in a tray, and then placing two trays in a computer cabinet to achieve over 100 petaflops per cabinet. With a 10-cabinet system, Tesla’s Dojo exapod will exceed the exaflop barrier of compute power.

Looking only at their subatomic particles, most materials can be placed into one of two categories.

Metals—like copper and iron—have free-flowing electrons that allow them to conduct electricity, while insulators —like glass and rubbe r— keep their electrons tightly bound and therefore do not conduct electricity.

Insulators can turn into metals when hit with an intense electric field, offering tantalizing possibilities for microelectronics and supercomputing, but the physics behind this phenomenon called resistive switching is not well understood.

Superconductors—found in MRI machines, nuclear fusion reactors and magnetic-levitation trains—work by conducting electricity with no resistance at temperatures near absolute zero, or −459.67°F.

The search for a conventional superconductor that can function at room temperature has been ongoing for roughly a century, but research has sped up dramatically in the last decade because of new advances in machine learning (ML) using supercomputers such as Expanse at the San Diego Supercomputer Center (SDSC) at UC San Diego.

Most recently, Huan Tran, a senior research scientist at Georgia Institute of Technology (Georgia Tech) School of Materials Science and Engineering, has worked on Expanse with Professor Tuoc Vu from Hanoi University of Science and Technology (Vietnam) to create an artificial intelligence/machine learning (AI/ML) approach to help identify new candidates for potential superconductors in a much faster and reliable way.

The supercomputer is part of the larger constellation of inter-connected supercomputers with a combined capacity of 36 exaFLOPS.

Abu Dhabi-based technology holding group G42 has unveiled the world’s fastest supercomputer, the Condor Galaxy-1 (CG-1), which has 54 million cores and a processing capacity of four exaflops, a press release said. The supercomputer is located in Santa Clara, California, and will be operated by Cerebras, a US-based AI firm under US laws.

As artificial intelligence (AI) technology takes center stage, there is a strong demand for supercomputers to help businesses train their own models. Companies like Microsoft have offered to build the extremely expensive infrastructure and rent it out for companies to work on them.

July 20 (Reuters) — Cerebras Systems on Thursday said that it has signed an approximately $100 million deal to deliver the first of what could be up to nine artificial intelligence (AI) supercomputers in a partnership with United Arab Emirates-based technology group G42.

The deal comes as cloud computing providers around the world are searching for alternatives to chips from Nvidia Corp (NVDA.O), the market leader in AI computing whose products are in short supply, thanks to the surging popularity of ChatGPT and other services. Cerebras is one of several startups looking to challenge Nvidia.

Silicon Valley-based Cerebras said that G42 has agreed to purchase three of what it calls its Condor Galaxy systems, all of which it will build in the U.S. to speed up the roll out. The first one will come online this year, with two more coming in early 2024.

The new supercomputer, made by the Silicon Valley start-up Cerebras, was unveiled as the A.I. boom drives demand for chips and computing power.

Elon Musk is pushing hard to complete development of its Full Self-Driving software and power forward with its Optimus robot program as it looks to celebrate its own “ChatGPT moment”.

It should be ready by the end of 2024 but can we take Musk’s word at face value when it comes to deadlines?

Tesla CEO Elon Musk has committed to spending a billion dollars to build the Dojo supercomputer over the next year, Bloomberg.

Continue reading “Elon Musk commits $1 billion toward building Tesla’s Dojo supercomputer” »

Over the past decade, teams of engineers, chemists and biologists have analyzed the physical and chemical properties of cicada wings, hoping to unlock the secret of their ability to kill microbes on contact. If this function of nature can be replicated by science, it may lead to development of new products with inherently antibacterial surfaces that are more effective than current chemical treatments.

When researchers at Stony Brook University’s Department of Materials Science and Chemical Engineering developed a simple technique to duplicate the cicada wing’s nanostructure, they were still missing a key piece of information: How do the nanopillars on its surface actually eliminate bacteria? Thankfully, they knew exactly who could help them find the answer: Jan-Michael Carrillo, a researcher with the Center for Nanophase Materials Sciences at the Department of Energy’s Oak Ridge National Laboratory.

For nanoscience researchers who seek computational comparisons and insights for their experiments, Carrillo provides a singular service: large-scale, high-resolution molecular dynamics (MD) simulations on the Summit supercomputer at the Oak Ridge Leadership Computing Facility at ORNL.