EL-RAKHAWI NEURO-PHOTONIC SILICON ARCHITECTURE: EMULATING BIOLOGICAL DENDRITIC COMPUTATION VIA NON-EQUILIBRIUM THERMODYNAMICS AND CAUSAL SPIKING NEURAL NETWORKS

Abstract

The El-Rakhawi Neuro-Photonic Silicon Architecture (NPSA) revolutionizes neuromorphic computing by emulating biological dendritic computation to overcome the Von Neumann bottleneck and extreme energy consumption of modern AI. It integrates four core modules: silicon-photonic memristive synapses for low-power Spike-Timing-Dependent Plasticity; dendritic optical interferometers combining linear Mach-Zehnder interferometers with non-linear phase-change elements for localized signal integration; Causal Spiking Neural Networks utilizing a Temporal Asymmetry Constraint to learn true cause-and-effect rather than statistical correlations; and non-equilibrium thermodynamic control to minimize Joule heating. Performance is quantified by the El-Rakhawi Neuro-Photonic Efficiency Metric (ENPEM), measuring causal information throughput per Joule. By bridging solid-state photonics, thermodynamics, and causal neuroscience, NPSA enables scalable, ultra-low-power edge AI for real-time applications, achieving energy efficiencies orders of magnitude beyond traditional digital processors while strictly adhering to physical limits.