Practical Boolean Backpropagation
Fuente:
arXiv
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| Auteur principal: | |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866910929910759424 |
|---|---|
| author | Golbert, Simon |
| author_facet | Golbert, Simon |
| contents | Boolean neural networks offer hardware-efficient alternatives to real-valued models. While quantization is common, purely Boolean training remains underexplored. We present a practical method for purely Boolean backpropagation for networks based on a single specific gate we chose, operating directly in Boolean algebra involving no numerics. Initial experiments confirm its feasibility. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_03791 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Practical Boolean Backpropagation Golbert, Simon Machine Learning Artificial Intelligence Boolean neural networks offer hardware-efficient alternatives to real-valued models. While quantization is common, purely Boolean training remains underexplored. We present a practical method for purely Boolean backpropagation for networks based on a single specific gate we chose, operating directly in Boolean algebra involving no numerics. Initial experiments confirm its feasibility. |
| title | Practical Boolean Backpropagation |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2505.03791 |