Extropic raises $14.1M to build ‘physics-based computing’ hardware for generative AI

Extropic raises $14.1M to build ‘physics-based computing’ hardware for generative AI

Extropic, a hardware startup led by former members of Alphabet Inc’s quantum computing research team, announced today that it has raised $14.1 million in seed funding.

Kindred Ventures led the investment. It was joined by HOF Capital, Julian Capital, Marque VC, OSS Capital, Valor Equity Partners, and Weekend Fund. Extropic says the round also saw participation from more than a dozen other backers, including executives from Adobe Inc. and Shopify Inc. and several venture-backed AI startups.

Extropic was founded last year by CEO Guillaume Verdon, who previously led the quantum computing team at Alphabet’s research unit X. Trevor McCourt, the company’s chief technology officer, was also a researcher at the search giant. While at Alphabet, Verdon and McCourt led the development of the TensorFlow library that can be used to run artificial intelligence models on quantum computing chips.

that it Believes Extropic is building a chip optimized for running large language models, or LLMs. in Blog post Today, Verdon described the company’s technology as a “new, integrated paradigm for physics-based computing” and detailed that it harnesses “non-equilibrium thermodynamics.” This indicates that the design of the Extropic chip includes concepts from Non-equilibrium thermodynamicsIt is an emerging branch of physics that focuses on the study of phenomena such as chemical reactions.

Verdon’s blog post noted that the company’s product is not a quantum computing chip. “As scalability timelines for quantum physics-based computers grow infinitely longer, many on our team have sought a different path to practical physics-based computing,” he wrote.

One of the main reasons why researchers have not yet succeeded in building a commercially useful quantum chip is that such processors are highly vulnerable to computing errors or noise. These errors make it impossible to perform complex calculations reliably. Extropic seeks to build a system where “noise is an asset, not a liability.”

The company did not share more detailed technical information about its technology. However, Verdon revealed that one of Extropic’s goals is to reduce the amount of electricity needed to power AI models. He also suggested that the company’s technology would automate some programming tasks, writing that “one can imagine a computer that, instead of being deterministically programmed, naturally finds a way to program itself to learn representations of the world.”

It is possible to infer additional details about Extropic technology from the fact that it is intended for running artificial intelligence models.

Neural networks process data using mathematical calculations called matrix multiplications. These are calculations performed on matrices, which are mathematical structures that include numbers organized into rows and columns like a spreadsheet. Because AI models extensively use matrix multiplication operations during data processing, almost all AI-optimized chips include circuitry optimized to perform such operations.

AI chips typically also include a large amount of high-speed memory. The reason is that AI models repeatedly move data to and from memory during processing. The faster the data can complete this round trip, the faster the neural network can generate results for users.

If the report that Extropic is developing an improved LLM processor is accurate, the company can expect to face competition from Nvidia Corp. foot A new data center processor, the H200, is specifically designed to run LLMs. It features twice the on-board memory as Nvidia’s previous flagship graphics card.

picture: Unsplash

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