Machine Learning Street Talk · Sunday, June 28, 2026
Thomas Ahle explains the concept of thermodynamic computing, where noise is intrinsic to the computation process, drawing parallels to probabilistic machine learning. This approach aims to model chips as stochastic differential equations, allowing noise to settle and achieve computational results that would otherwise be prohibitively expensive.
“In chip design, noise is the enemy. Manufacturers spend a fortune getting rid of it. But in thermodynamic computing, the opposite is kind of true. The noise is the computation.”
“And thermodynamic computing tries to make the chip itself as a stochastic differential equation. Let the chips' own noise settle into place, and then it can land on answers that would normally cost a fortune to compute.”