Abstract
Modern artificial intelligence architectures based on floating-point matrix multiplications face rapid growth in energy consumption with scaling and the von Neumann bottleneck. This paper presents a non-classical computing model, Voxel Oscillator Computing (VOC AI), based on a 12-vector highly isotropic (within discrete lattice constraints) phase matrix (LOGOS/MOUTION), extending the established S-HVE (Harmonic Vector Equilibrium) and Resonant AI frameworks. The architecture uses a face-centered cubic (FCC) lattice with 12 balanced connections, where information is encoded as a phase (16-bit integer) without explicit amplitude weights. In 2D planar projections, this natively resolves into a 6-connected hexagonal lattice. The MOUTION algorithm implements discrete phase dynamics without floating-point operations, using only a normalized sine lookup table (LUT) and integer arithmetic. The system demonstrates O(N) global scaling with strictly O(1) local complexity, effectively mitigating the memory-processor separation, and significantly reduces theoretical energy consumption compared to GPU alternatives. A complete description of the topology, update equations, isotropic spatial navigation, and a reproducible C implementation are provided to establish prior art.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Lozovyi, Olexandr, "Voxel AI Architecture Based on 12-Vector Highly Isotropic Phase Matrix (LOGOS/MOUTION)", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/9787