Asynchronous DiffLogic CA requires 50 steps vs 20 steps for synchronous training to reconstruct checkerboard pattern

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• DiffLogic CA with 128-bit cell state and 12 steps successfully learns 20x20 lizard pattern, generalizing to 40x40 gridfinding0.795
  Demonstration of DiffLogic CA on complex non-regular shapes with arbitrary memorization requirements
• Synchronously trained DiffLogic CA circuit succeeds at asynchronous inference without retrainingfinding0.794
  Unexpected result demonstrating robustness of learned circuits beyond their training regime
• Checkerboard circuit trained on 16x16 grid successfully generalizes to 64x64 grid with 4x more time stepsfinding0.780
  Grid scaling generalization result demonstrating boundary-size invariance
• Asynchronously trained DiffLogic CA shows greater robustness to 10x10 pixel damage than synchronously trained version, measured by sum of absolute differencesfinding0.777
  Quantitative comparison of synchronous vs asynchronous training for noise resilience
• DiffLogic CA fully converges to 16x16 checkerboard target pattern with both soft and hard losses reaching zerofinding0.776
  Core result of pattern generation experiment demonstrating recurrent circuit learning
• Asynchronous training improves robustness of DiffLogic CA circuits to perturbations compared to synchronous trainingclaim0.773
  Interpretive claim supported by damage resilience experiment results
• DiffLogic CA with 64 channels successfully learns 16x16 colored Google G pattern in 15 steps using 8-color binary palettefinding0.767
  Demonstration of multi-channel RGB color pattern generation with binary states
• DiffLogic CA can directly learn the local rules needed to achieve desired macroscopic computation, addressing the fundamental challenge identified by Toffoli and Margolusclaim0.737
  Core thesis of the paper framed against the historical challenge of hand-crafting CA rules