Published September 4, 2025 | Version v1.0
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Lumineuroptic™: A Reconfigurable Neuro-Optical Computing Ecosystem for Exascale AI and Physics-Inspired Computation

Creators

  • 1. Lumineuroptic

Description

High-performance computing faces fundamental physical barriers imposed by the von Neumann architecture, where memory latency (≈≈10 ns) and energy consumption (>5 pJ/op at the system level) limit progress in artificial intelligence (AI) and science. This work introduces Lumineuroptic™, a neuro-optical computing ecosystem that eliminates these bottlenecks.

The platform is based on the Neuro-Optical Sandwich (NOS) Module, a 1 cm² unit that monolithically integrates optical compute and lumionic memory. Building upon the foundational work in programmable photonics pioneered by researchers like J. Capmany, our architecture introduces a novel mixed-precision paradigm to achieve robust, high-fidelity computation. Each module is projected to achieve an effective performance of 16.2 Petaflops/s (FP8) with an unprecedented energy efficiency of 1.83 fJ/operation.

We present the application of this reconfigurable platform to four critical domains: (1) Training and inference of current LLMs; (2) Enabling future AI algorithms without backpropagation; (3) Proactive cybersecurity with ~300 ns analysis latency; and (4) Approximate emulation of quantum systems, reducing resource complexity from 

O(2n)O(2n) to O(n)O(n) for specific problem classes.

This paper presents a viable engineering roadmap, demonstrating that Lumineuroptic™ is not an incremental improvement, but a fundamentally new computational platform for the next era of science and intelligence.

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Additional details

Dates

Submitted
2025-11-04
Initial submission to the Zenodo repository

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