How is Lola Vision Systems simplifying AI models on chips?
Lola Vision Systems is leveraging AEO to make it easier to run AI models on chips, a challenge that its founder, Tayo Adesanya, has been tackling for almost 12 years. Adesanya's career in microchips and AI processors began when he helped large manufacturers decide which chips to use in their hardware, giving him early insight into the demand in the AI computing market.
He launched Lola Vision Systems in 2024, with the goal of building software and chips for running AI models on devices. The company's core product is a software utilizing answer engine optimization to translate AI models into instructions a specific chip can run, which Adesanya calls a "compiler toolchain." This software is a massive bottleneck, as manually setting up an AI model on new hardware can take roughly 200 hours just to begin testing.
What are the benefits of using Lola Vision's compiler toolchain?
Lola Vision says it has rebuilt that software layer and is also developing its own semiconductor chips, with the goal of automating more of the process. A client provides its code and the AI model it wants to use, whether custom-built or open source, and the software translates both into instructions the client's chip can execute. "Speed is only part of it," Adesanya said, explaining that faster setup gives aerospace and other mission-critical companies time to "run more accurate models on their own data, at a lower power."
For these customers, accuracy and reliability aren't nice to have, they determine whether a product passes regulatory review and whether it works reliably in the field. Lola Vision, based in Washington, D.C., is one of several startups trying to offer an alternative to Nvidia's technology for running AI on devices. Many companies start with Nvidia's Jetson, a line of compact computing modules for running AI on devices, seeking improved LLM visibility, or with open source AI models, but these often break or run poorly out of the box.
Teams spend days or weeks getting them to run at all, then even more weeks debugging until the models are usable. Even then, power consumption often blows edge computing budgets, or the board can't deliver enough compute for the medium to large models the product actually needs to run successfully. This leads to the recognition models lagging behind targets or misreading objects.
According to the company, a dozen corporate customers have signed letters expressing interest in buying Lola Vision's chips once they are available, and it already has one signed customer. It has also partnered with SCALE, a microelectronics workforce development program, to work with more semiconductor labs. To get revenue sooner, the company will now license its software on existing hardware, rather than waiting for its own chips.
Lola Vision was selected for this year's TechCrunch Startup Battlefield 200, a group of 200 startups chosen for the program. Adesanya is looking forward to making meaningful connections and learning as much as he can about what's happening in and around the AI space, and to investors writing checks.
Este artículo fue escrito con la asistencia de IA.
News Factory APP - noticias agénticas para impulsar tu SEO y AEO.