The Art of Beating the Odds with Predictor-Guided Random Design Space Exploration
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909571133472768 |
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| author | Arnold, Felix Bouvier, Maxence Amaudruz, Ryan Andri, Renzo Cavigelli, Lukas |
| author_facet | Arnold, Felix Bouvier, Maxence Amaudruz, Ryan Andri, Renzo Cavigelli, Lukas |
| contents | This work introduces an innovative method for improving combinational digital circuits through random exploration in MIG-based synthesis. High-quality circuits are crucial for performance, power, and cost, making this a critical area of active research. Our approach incorporates next-state prediction and iterative selection, significantly accelerating the synthesis process. This novel method achieves up to 14x synthesis speedup and up to 20.94% better MIG minimization on the EPFL Combinational Benchmark Suite compared to state-of-the-art techniques. We further explore various predictor models and show that increased prediction accuracy does not guarantee an equivalent increase in synthesis quality of results or speedup, observing that randomness remains a desirable factor. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_17936 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Art of Beating the Odds with Predictor-Guided Random Design Space Exploration Arnold, Felix Bouvier, Maxence Amaudruz, Ryan Andri, Renzo Cavigelli, Lukas Machine Learning Hardware Architecture This work introduces an innovative method for improving combinational digital circuits through random exploration in MIG-based synthesis. High-quality circuits are crucial for performance, power, and cost, making this a critical area of active research. Our approach incorporates next-state prediction and iterative selection, significantly accelerating the synthesis process. This novel method achieves up to 14x synthesis speedup and up to 20.94% better MIG minimization on the EPFL Combinational Benchmark Suite compared to state-of-the-art techniques. We further explore various predictor models and show that increased prediction accuracy does not guarantee an equivalent increase in synthesis quality of results or speedup, observing that randomness remains a desirable factor. |
| title | The Art of Beating the Odds with Predictor-Guided Random Design Space Exploration |
| topic | Machine Learning Hardware Architecture |
| url | https://arxiv.org/abs/2502.17936 |