Colloidal boltzmann machine: An energy-efficient natural computing

The escalating computational demands of deep learning have raised serious energy concerns, calling for urgent solutions in green artificial intelligence (AI). In this paper, we introduce the Colloidal Boltzmann Machine (CBM), a computing architecture based on an ensemble of nearly independent colloidal particles that can switch between binary active and inactive states. This architecture builds on a key insight from colloid science that the Gibbs factor N! remains essential even for classically distinguishable particles. Specifically, by incorporating this factor into the entropy formulation of colloidal particles, we derive an irreducible baseline energy in the classical regime, which is inherent to the presence of thermal fluctuations. We show that, under thermal fluctuations, the CBM spontaneously evolves toward its global energy minimum, thereby overcoming a limitation of certain conventional models, which can become trapped in local optima. Crucially, this baseline energy prevents complete system shutdown, enabling the potential for self-sustaining computational operation. The CBM thus offers a potential pathway toward energy-efficient, always-on natural computing hardware.