Strong Compute, a Sydney, Australia-based startup that helps developers eliminate bottlenecks in their machine learning training pipelines, announced today that it has raised a $7.8 million seed round. The round includes a total of 30 funds and angels, including Sequoia Capital India, Blackbird, Folklore, and Skip Capital, as well as Y Combinator, Starburst Ventures, and founders and engineers from companies such as Cruise, Waymo, Open AI, SpaceX, and Virgin Galactica. The company, which was part of Y Combinator’s Winter ’22 batch, promises that its optimizations can speed up the training process anywhere from 10 to 1,000 times, depending on the model, pipeline, and framework. As Strong Compute founder Ben Sands, who previously also co-founded augmented reality company Meta, told me, the team recently made some progress where they were able to take Nvidia’s reference implementation, which used their LayerJot client, to run 20 times faster.Image credits: Strong Compute “It was a great win,” said Sands. “It really gave us the feeling that there is nothing that cannot be improved.” He didn’t want to reveal all the details of how the team’s optimizations worked, but he did point out that the company is now hiring mathematicians and building tools that give it a more granular view of how user code interacts with CPUs and GPUs at a much higher level. deeper than was previously possible. As Sands highlighted, the company’s current focus is to start automating much of the current work to streamline the training process, and that’s something the company can now address thanks to this funding round. “Our goal now is to have serious development partners in autonomous driving, medical, and aviation, so we can see what will really go mainstream very well,” he explained. “Now we have the resources to have an R&D team that doesn’t have to deliver something in a two-week sprint, but can actually see what real core technology might take a year to get a win from, but that can really help. with that automated problem analysis.” The company currently has six full-time engineers, but Sands plans to double that number in the coming months. In part, that’s also because the company is now getting interest from large companies that often spend $50 million or more on their computing resources (and Sands noted that the market is essentially bimodal, with customers spending less than $1 million or $10 million). millions). to $100 million, with only a few players in between).
Image credits: Strong computation However, every company trying to build ML models suffers from the same problem: training models and running experiments to improve them still takes a long time. That means well-paid data scientists working on these problems spend a lot of time in a holding pattern, waiting for results to come in. “Strong Compute is solving the basketball court problem,” said SteadyMD Chief Financial Officer Nikhil Abraham. “Long training times kept our best developers shooting hoops all day, waiting on machines.” And while some of that incoming interest is coming from the financial industry and businesses that want to optimize their natural language processing models, Strong Compute’s focus remains on machine vision for now. “We have only scratched the surface of what machine learning and AI can do.” said fellow folklore Tanisha Banaszcyk. “We love working with founders who have long-term ambitions and visions that will last from generation to generation. Having invested in autonomous driving, we know how important speed to market is and we see the impact Strong Compute can have in this market with its purpose-built platform, deep understanding of the $500 billion market, and world-class team.” .
