Scalable Interconnect Learning in Boolean Networks
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911160364695552 |
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| author | Kresse, Fabian Yu, Emily Lampert, Christoph H. |
| author_facet | Kresse, Fabian Yu, Emily Lampert, Christoph H. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_02585 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Scalable Interconnect Learning in Boolean Networks Kresse, Fabian Yu, Emily Lampert, Christoph H. Machine Learning Logic in Computer Science 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. |
| title | Scalable Interconnect Learning in Boolean Networks |
| topic | Machine Learning Logic in Computer Science |
| url | https://arxiv.org/abs/2507.02585 |