FRACTAL MEMORY ON A HYPERSPHERE: Hybrid Optical Neuron Architecture for Extreme Data Compression

FRACTAL MEMORY ON A HYPERSPHERE represents an emerging paradigm in neuromorphic computing, inspired by the self-similar structures of fractal geometry. This article presents a hybrid hardware architecture based on an optical neuron with polarization filters and directional chromatic photodiodes, integrated onto a physical hypersphere with atomic platinum layers. Utilizing double stereographic projections on the Riemann sphere and quaternion extensions for 4D rotations, the system enables efficient hierarchical storage with lossless compression on the order of 1000:1. Numerical simulations validate the model, demonstrating a 45% reduction in interferences and an SNR of ~25 dB at 1 GHz. Applications target edge AI, computer vision, and cognitive modeling, paving the way for adaptive low-power chips.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-10-25
DOI
https://doi.org/10.5281/zenodo.17496682
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
0.00
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article

FRACTAL MEMORY ON A HYPERSPHERE: Hybrid Optical Neuron Architecture for Extreme Data Compression

Zenodo (CERN European Organization for Nuclear Research)
Neural Networks and Reservoir Computing
article

FRACTAL MEMORY ON A HYPERSPHERE: Hybrid Optical Neuron Architecture for Extreme Data Compression

article en

Abstract

FRACTAL MEMORY ON A HYPERSPHERE represents an emerging paradigm in neuromorphic computing, inspired by the self-similar structures of fractal geometry. This article presents a hybrid hardware architecture based on an optical neuron with polarization filters and directional chromatic photodiodes, integrated onto a physical hypersphere with atomic platinum layers. Utilizing double stereographic projections on the Riemann sphere and quaternion extensions for 4D rotations, the system enables efficient hierarchical storage with lossless compression on the order of 1000:1. Numerical simulations validate the model, demonstrating a 45% reduction in interferences and an SNR of ~25 dB at 1 GHz. Applications target edge AI, computer vision, and cognitive modeling, paving the way for adaptive low-power chips.

Zenodo (CERN European Organization for Nuclear Research)
Affordable and clean energy
Openalex Percentile: Top 23%
Neural Networks and Reservoir Computing
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