Extropic, who have developed a new type of computer chip that operates differently from traditional chips. They start by explaining that the semiconductor industry and AI development are facing a challenge known as "Moore's Wall," which is a limit to how small transistors in chips can be made. This is a problem because AI demands are increasing, requiring more power and larger models.
Moore's Law, which has driven innovation in technology by making transistors smaller and more efficient, is coming to an end because transistors are reaching a size where they can't function reliably due to thermal fluctuations at the atomic level.
The team at EX Tropic proposes a solution by embracing the stochastic (random) nature of physics at small scales. Instead of fighting the randomness, they want to use it to their advantage in AI algorithms. Traditional AI algorithms add randomness artificially, but their idea is to use the natural randomness of electrons in their chip design.
They've created a chip that uses superconductors, which are materials with no electrical resistance, making them highly efficient. These chips are designed to be stochastic and analog, meaning they can handle randomness and continuous values, unlike digital chips that work with discrete values (0s and 1s).
Their chips are capable of accelerating sampling, a process important in probabilistic models used in AI. Sampling on traditional digital computers is energy-intensive and slow because it requires complex circuits to generate pseudo-randomness. EX Tropic's chips, however, use the natural randomness in the movement of electrons to perform sampling more efficiently.
The team believes that their approach, which they call "physics-based computing," can lead to significant advancements in AI by allowing for more complex models to be run more efficiently. They hope that their launch will attract talented individuals in machine learning and hardware development to join them in scaling this new technology.
They also discuss the broader implications of their work, suggesting that if current AI development continues on its current path with traditional hardware, it will face significant bottlenecks. They believe their approach can help overcome these challenges by going back to the physical principles of semiconductors and exploring new ways to harness their potential.
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