Hardware Performance Characterization of a Closed-Form Complex-Domain Encoder: Compute, Memory, Energy, and Latency for Real-Time and Edge Deployment — Supplementary Reproducibility Archive

Supplementary reproducibility archive for Paper 6 of the spiral-domain encoder validation campaign, targeting IEEE Transactions on Parallel and Distributed Systems (manuscript in preparation).Coverage. 10 pre-registered studies (Studies 79–82 from Phase XV; Study 90 from Phase XVIII; Study 92 from Phase XIX; Studies 95–97 from Phase XX of the spiral-domain encoder validation campaign). 30 hypotheses, 26 SUPPORTED (87%), 4 honest bounded negatives substantively interpreted.Substantive findings. NumPy CPU throughput plateau 47,500 subj/s; PyTorch MPS GPU plateau 220,000 subj/s; CPU/MPS crossover at K ≈ 30; single-frame latency p99 = 12.13 μs, p99.9 = 34.13 μs (29× under 1 ms surgical RT budget); linear T-scaling slope -0.978 (vs theoretical -1.0); 0.07 mJ/subject on MPS GPU (4–400× more efficient than typical edge-ML inference); INT16 encoded-state and INT8 federated-coefficient compatibility for Cortex-M class embedded deployment.Contents.• README.md — submission-package map and reproduction instructions• preregistrations/ — frozen pre-registration .md documents with literal-threshold decision rules• reports/ — per-study .md verdict reports against frozen rules + phase summaries• runners/ — deterministic Python runners (PYTHONHASHSEED=0) reproducing every reported measurement• raw_data/ — per-study CSV outputs and machine-readable JSON verdict blocks• figures/ — manuscript figures (PNG, 300 DPI) + figure-build Python script• code/ — encoder source code (spiral.py), supporting modules (baselines.py, data.py), and package __init__ filesReproducibility. Full validation pipeline is reproducible end-to-end under PYTHONHASHSEED=0 on a standard Python 3.9+ installation with NumPy 2.0+ linked against the Apple Accelerate framework, plus PyTorch 2.8+ with MPS backend for the GPU portions. Reference machine: Apple Silicon arm64 (M-series), macOS 14. See README.md for per-study run commands.Methodological discipline. Every hypothesis was pre-registered with externally anchored decision rules frozen prior to runner execution. Zero post-hoc threshold adjustments were applied. Honest bounded negatives are interpreted substantively rather than discarded.Related companion archives. Paper 1 (10.5281/zenodo.20129137), Paper 2 (10.5281/zenodo.20138786), Paper 3 (10.5281/zenodo.20139171), and the corresponding Papers 4, 5, 7 archives in this same Zenodo collection.