Waveform compression: clever per-signal models gain little; structure and the design itself carry the big wins

Measured on a synthetic multi-core SoC dump (~1.7 GB FSDB, ~6 G changes), in bytes per change at zstd-3 / zstd-19 as the entropy stage: - periodic predictor (predict from change i-2, store exceptions): 0.53 / 0.37 - XOR vs the previous value: 0.41 / 0.28 - dictionary (repeat index or XOR literal): 0.36 / 0.26 - best-of per signal: 0.35 / 0.25 (only ~3% over dictionary alone) - plus exact-stream dedupe across signals: 0.19 / 0.14

Relations mined from the data without RTL: identical streams were ~42% of bytes, and time-shifted copies (pipeline registers) ~3% more. Bit-slices and inverted copies were negligible. Verify every relation change by change before dropping a stream.

Design-aware: storing only what the dumper stores and computing combinational signals from the RTL makes those signals cost zero bytes. Coding packed-struct fields separately and sharing timestamps per clock domain gave roughly another 25%. But the changed-field decode must be word-level, and should be chosen per struct only where it saves more than ~25%; busy structs get bigger and slower otherwise.

Read-side tip: with 16K-change blocks, rebuild values only up to the end of the query window and convert only the window. That cut point-query cost ~3x with no format change. Smaller blocks (4K) cut it further but cost ~39% more size.

Untested: context-model coders with arithmetic coding in place of zstd (a plausible 10-30% at much higher CPU). The theoretical floor is tiny, since RTL plus inputs determine the run. So past this point, gains come from re-simulation (checkpoints plus inputs), not better coding.