Deterministic mapping
Test method
repeatability run set
Gate
Input class variance < 1.5% across 1000 cycles
Research overview
Physical photonic operators for machine learning inference
This research brief summarizes the current technical thesis, operating assumptions, validation method, and known constraints for PiP-based inference systems. It is designed as a compact orientation document for researchers and engineering partners.
Executive brief
Research problem: repeated digital context reconstruction inflates latency and energy in persistent workloads.
Hypothesis: learned photonic operators can execute core transforms with lower update cost and improved continuity.
Method: train in differentiable simulation, compile into geometry, fabricate PiPs, validate with benchmark gates.
Scope: persistent, context-heavy inference classes rather than universal general-purpose compute replacement.
Abstract
CommonAccess studies a substrate where learned inference behavior is encoded into photonic geometry. Instead of repeated digital arithmetic, inference is executed as propagation in structured material, then decoded through calibrated readout.
The core claim is not universal replacement of digital accelerators. The claim is that specific workloads with persistent state and heavy context reuse can benefit when transform execution is relocated from instruction loops into stable physical response.
Development is constrained by measurable gates: deterministic mapping, rank/separability, temporal stability, and parity-quality benchmark evaluation against conventional GPU/NPU paths.
Method snapshot
The research method remains compiler- and validation-centric: model intent is converted into physical operator candidates, tested in differentiable simulation, fabricated under process constraints, and validated before integration.
Training complexity remains at design time. Fabricated operators contain learned mappings without retaining datasets or digital weight files at inference time.
FlowStateMeasurementConstraint
Assumptions
Operator behavior can be represented in differentiable physical simulation.
Fabricated structures can retain mapping fidelity within tolerance windows.
Sparse readout remains sufficient for target output decoding accuracy.
Hybrid optical-digital orchestration preserves deployable software interfaces.
Thermal and material drift can be bounded through calibration and runtime guardrails.
Benchmark claims are valid only when output quality parity is maintained.
Limits
Not a universal replacement for general-purpose compute kernels.
Performance depends on workload fit and operator validity gates.
Fabrication variance and drift management remain active research constraints.
Some functions (normalization, control policy, orchestration) remain digitally implemented in early release classes.
Mode scaling is gated by crosstalk control, calibration retention, and manufacturing yield.
Current status and validation focus
Current work centers on structured validation: deterministic response checks, rank and separability analysis, temporal stability under perturbation, and benchmark parity protocols.
Promotion from research to broader deployment requires successful gate completion across representative persistent workloads and constrained thermal-power envelopes.
High-rank dimensionality
Test method
SVD on output state matrix
Gate
Effective rank exceeds benchmark floor per task family
Linear separability
Test method
readout classifier probe
Gate
Probe accuracy above digital baseline threshold
Temporal stability
Test method
drift and hysteresis monitoring
Gate
Stability window remains within calibration band
Reproducibility notes
Validation method: input pattern sweeps, output capture, SVD rank, classifier probe accuracy.
All scale-up decisions depend on deterministic and temporal stability gates.
Failure to meet gate criteria triggers design iteration, not deployment escalation.
Benchmark reports must include dataset identity, run manifest, calibration profile, and baseline-relative deltas.
References and terminology
Reference set includes internal operator validation protocols and physics-compiler integration documents.
For collaboration access to technical documents: agni@comac.network