PRISM：用光纤和激光构建的光子神经网络，让AI以光速思考

PRISM is a coherent fiber-based neural network simulator that uses 1550nm laser and IQ modulation to perform matrix operations in single-mode fiber. It achieves 100% accuracy on four telecom classification tasks and provides a verifiable software twin for photonic AI chips. Notably, it has no GPU dependency and can run on ordinary laptops.

Modern silicon-based AI faces physical limits: matrix multiplication consumes significant energy, electrons move at only 1% of light speed, and memory bandwidth becomes a bottleneck. PRISM explores photon-based computation to overcome these issues—using light to perform calculations at light speed with theoretically 1000x lower energy consumption.

PRISM's core methods include:

1. Physical computation: Uses 1550nm coherent light in single-mode fiber; IQ modulation encodes weights (amplitude) and signs (phase: 0 for positive, π for negative); dispersion-compensating fiber (DCF) implements dot product via Maxwell's equations; zero-difference detection reads results; dense wavelength division multiplexing (WDM) enables parallel neuron activation.
2. Recursive meta-prompt: Deterministic weight generation via 3 levels (Fibonacci seed → meta-generator → weight matrix) with light memory to accumulate context.
3. Recursive engine: Self-improvement through generation-based expansion—even generations deepen (insert fiber loops), odd generations widen (expand narrow layers) to reach optimal accuracy.

PRISM validates its effectiveness with:

1. 100% accuracy: Four telecom tasks (multi-damage classifier, adaptive modulation controller, OTDR fault diagnosis, DCI link state classifier) achieve 100% fiber accuracy.
2. Test validation: Passes all 50 tests covering fiber physics, EDFA, DCF, IQ modulation, zero-difference receiver, and end-to-end applications.
3. Accessibility: No GPU, CUDA, PyTorch, or TensorFlow dependencies—runs on any machine from the past decade.

PRISM's significance:

1. Software twin: Enables researchers to predict photonic AI chip behavior before manufacturing, saving costs.
2. Break silicon limits: Demonstrates photon computation's potential for speed (light speed) and energy efficiency (1000x lower than GPU).
3. Recursive insight: Recursive self-improvement is seen as a base for advanced AI, not just a technical feature.

To try PRISM:

1. Clone the repo: git clone https://github.com/infinitule/PRISM.git
2. Install dependencies: pip install numpy
3. Run:
   • Recursive engine: python3 main.py
   • Telecom use cases: python3 use_cases.py No extra dependencies needed—works on any recent machine.