ruvector/crates/photonlayer-core

PhotonLayer

A deterministic optical AI front end. A learned phase mask performs task-specific analog preprocessing on incoming light, so the sensor records a small compressed measurement and a tiny digital decoder reads the answer.

The camera no longer captures the image. It captures the answer shaped by physics.

Or, technically: PhotonLayer learns what light should measure before silicon ever sees the data.

The wedge: auditable optical compression for task-useful sensing

This is not "an optical neural network." It is narrower and far more defensible:

1. Task-first — the mask is trained for the downstream objective, not generic image reconstruction.
2. Compression-first — the flagship result compresses a 16×16 input to a handful of sensor pixels.
3. Privacy by physics — classify/verify from an optical measurement that need not look like the scene.
4. Deterministic receipts — every run is reproducible and bound to BLAKE3 receipts (audit trails).
5. Rust-native — pure Rust across simulation, training, WASM, CLI, and (eventually) hardware control.

Measured: compression sweep (more-data benchmark)

Synthetic 4-class task, 16×16 input (256 px), 40 samples/class, learned mask vs random mask vs a digital tiny-sensor baseline. From cargo test -p photonlayer-bench --release --test more_data_bench:

Sensor	pixels	reduction	learned	random	digital baseline
2×2	4	64×	0.988	0.738	0.688
3×3	9	28×	1.000	0.938	1.000
4×4	16	16×	1.000	0.950	1.000
1×1	1	256×	0.250	0.250	0.250 (chance — too tight)

At 64× pixel reduction the learned optical mask reaches ~99% where a random mask gets ~74% — the learned front end preserves task-useful information that random projection and naive sub-sampling lose. (1 pixel is below the task's information floor; both collapse to chance — reported honestly.)

Crates

Crate	Role
photonlayer-core	Scalar diffraction sim — complex/FFT, field/phase-mask, Fresnel/Fraunhofer/angular-spectrum propagation, detector (shot/read noise, binning, quantization), metrics, BLAKE3 receipts
photonlayer-bench	Learned-vs-random-vs-direct baselines, decoder, privacy/verification, synthetic data
photonlayer-cli	Command-line demos
photonlayer-ruvector	ruvector integration — coherence, boundary, embeddings, experiment memory, receipts
photonlayer-wasm	Browser execution (the eventual GitHub Pages demo)

Quick start

cargo test -p photonlayer-core                                                  # 23 unit tests + doctest
cargo run  -p photonlayer-bench --bin photonlayer-bench --release -- all        # flagship benchmark
cargo test -p photonlayer-bench --release --test more_data_bench -- --nocapture # compression sweep

Honest scope — what not to claim yet

Until hardware proves it, avoid: "ultra-low-power" product claims, "medical diagnostic", "autonomous- vehicle ready", "face recognition", "unbreakable privacy", "all-optical neural network". Use instead: lower digital-compute potential, task-useful optical compression, privacy-reduced measurement, research-grade imaging simulator, consented verification. Position medical/AV uses as research infrastructure and preprocessing, not decision automation. First commercial wedge: industrial & scientific sensing.

See docs/research/photonlayer/ASSESSMENT.md for positioning, use-case fit, the energy model + harder-dataset + reconstruction-attack roadmap, and references. Design: ADR-260, ADR-261.

License

MIT © Ruvector Team.