Lumineuroptic: A neuro-optical chip ~260x more efficient than an H100 (Paper)
2 BrunoMarti 10 9/5/2025, 4:15:52 PM zenodo.org ↗
Comments (10)
danielmarti · 3h ago
Excellent paper. It's great to see a system-level architectural proposal that integrates concepts from silicon photonics, in-memory computing, and mixed-precision design. The approach of using analog accumulation to improve SNR is a very clever solution to the noise problem in optical systems. The references are solid, and I appreciate the nod to foundational work in the field. I'll be following this project closely
saracas · 3h ago
This is fascinating, but the claims are bold. The ~1.83 fJ/op efficiency is several orders of magnitude beyond current electronics. The paper mentions a mixed-precision approach to deal with analog noise, but I'm curious about the real-world ENOB (Effective Number of Bits) they expect to achieve at the system level. Has the author considered the impact of manufacturing variations on the LMA's photonic resonators? Still, a very thought-provoking architecture.
jhontreley · 3h ago
Finally, someone is tackling the von Neumann bottleneck from first principles. The performance metrics are impressive, but the real game-changer for AI is the ~0.69 ns latency per layer. This could unlock new types of real-time inference and reinforcement learning models that are impossible today. The idea of a 100-module cluster training a 175B model in days with less than 4kW is just mind-blowing. This is the kind of hardware AGI research needs.
BrunoMarti · 6h ago
"Hi HN, I'm the author of the paper.
For the last couple of years, I've been working on a new computing architecture to solve the von Neumann bottleneck. The result is Lumineuroptic™, a neuro-optical system that, according to my theoretical analysis, offers orders-of-magnitude improvements in performance and efficiency over current hardware.
The paper details the architecture, the physics, and the performance projections (~16.2 PFLOPS/cm², ~1.83 fJ/op). I've also filed patents for the core technology.
I've created a more visual summary and a project website here: https://www.lumineuroptic.com
This is the work of an independent researcher, so I'm incredibly excited to share it with this community. I'm here to answer any questions you might have.
Thanks for reading!"
jhontreley · 3h ago
This goes way beyond just faster AI. The applications for quantum emulation and real-time cybersecurity are massive. The paper's vision of a 'Data Flow Guardian' analyzing network traffic at line speed is particularly compelling. This is the kind of foundational technology that could enable things like high-fidelity neural interfaces or the autonomous compute needed for off-planet missions. Very exciting work.
jhontreley · 3h ago
Excellent paper. It's great to see a system-level architectural proposal that integrates concepts from silicon photonics, in-memory computing, and mixed-precision design. The approach of using analog accumulation to improve SNR is a very clever solution to the noise problem in optical systems. The references are solid, and I appreciate the nod to foundational work in the field. I'll be following this project closely
angeloti · 6h ago
Interesting
danielmarti · 6h ago
nice idea, wow.
danielmarti · 6h ago
tell me more!
BrunoMarti · 6h ago
Thanks for the interest! I'd be happy to.
At its core, Lumineuroptic™ is a new computing architecture designed to solve the fundamental bottlenecks that limit current AI hardware like GPUs. Instead of improving the old system, I went back to first principles.
The core innovation is a "Neuro-Optical Sandwich" (NOS), a 1 cm² module that does three things fundamentally differently:
It computes with light, not just electrons. We use a dense array of 10 million micro-LEDs for massively parallel processing at 1.5 GHz.
It eliminates the memory wall. It features an in-memory computing layer (the Lumionic Memory Array or LMA) where memory access and computation are a single, ultra-fast operation (~0.69 ns). This avoids the ~10 ns latency penalty of accessing external HBM memory.
It scales with massive bandwidth. Modules are stacked in 3D and communicate via optical TSVs at >15 Tbps, creating a dense compute fabric.
The result, based on theoretical analysis, is a module that is ~21x faster (in terms of effective PFLOPS) and ~260x more energy-efficient than a state-of-the-art H100 GPU.
The full paper is on Zenodo, but I've created a more visual one-page summary on the project website: [Enlace a tu micrositio lumineuroptic.com]
Happy to answer any more specific questions!