P.E.R.C. prepares and sends a large packet of data. It starts with a description of how star systems form, and runs all the way through to how to analyze historical climactic changes by considering weathering patterns on exposed rocks. Each section starts with a plain-language overview of the topic, what data can be inferred from this kind of model, and why it would be interesting followed by specific decision procedures — the metaphorical big brother of the humble flowchart — on how to calibrate, cross-check, and utilize P.E.R.C.-class scientific instruments to build historical models of system and planet formation, and to identify anomalies. Then each section ends with a small, highly-compressed, task-specialized neural net that encodes an approximation to the full decision procedure for implementation in hardware.
P.E.R.C.'s recounting has a particular emphasis on detecting and analyzing potential bio-signatures, and is optimized for power- and sample- efficiency over time-efficiency. The design notes do list a few ways to revise the decision procedures for other desired criteria, though.
"The way I typically use this is to use my approximate hardware analyzers to scan over large quantities of data, such as full sky-scans, and then promote the most important, interesting, or anomalous data to conscious consideration to minimize the impact of approximation errors," it explains.