The Art of Beating the Odds with Predictor-Guided Random Design Space Exploration
This work addresses a critical need for high-performance, low-power, and cost-effective digital circuits, though it appears incremental as it builds on existing synthesis methods.
The paper tackles the problem of improving combinational digital circuits by introducing a predictor-guided random exploration method for MIG-based synthesis, achieving up to 14x synthesis speedup and 20.94% better minimization compared to state-of-the-art techniques.
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.