Scalable Interconnect Learning in Boolean Networks

Fabian Kresse; Emily Yu; Christoph H. Lampert

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arXiv · 2507.02585

Scalable Interconnect Learning in Boolean Networks

Fabian Kresse·Emily Yu·Christoph H. Lampert

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Abstract

Learned Differentiable Boolean Logic Networks (DBNs) already deliver efficient inference on resource-constrained hardware. We extend them with a trainable, differentiable interconnect whose parameter count remains constant as input width grows, allowing DBNs to scale to far wider layers than earlier learnable-interconnect designs while preserving their advantageous accuracy. To further reduce model size, we propose two complementary pruning stages: an SAT-based logic equivalence pass that removes redundant gates without affecting performance, and a similarity-based, data-driven pass that outperforms a magnitude-style greedy baseline and offers a superior compression-accuracy trade-off.

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Fabian Kresse, Emily Yu, Christoph H. Lampert. 2025-07-03. Scalable Interconnect Learning in Boolean Networks. https://arxiv.org/abs/2507.02585

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