✨ TL;DR
AutoPPA is an automated framework for optimizing circuit performance, power, and area (PPA) that learns optimization rules by contrasting code pairs rather than relying on manual rules. It outperforms existing methods including manual optimization and state-of-the-art automated approaches.
Optimizing performance, power, and area (PPA) in RTL (Register Transfer Level) circuit design is a critical but challenging task that requires deep understanding of circuit functionality and how different circuit structures affect PPA metrics. Existing automated approaches using large language models fall short in efficiency: feedback-based methods operate without prior knowledge and must explore solutions blindly, while knowledge-based methods depend heavily on manually-crafted optimization rules that are time-consuming to create and may not cover all optimization patterns. This creates a bottleneck in the design process where neither purely exploratory nor purely knowledge-driven approaches can efficiently find optimal circuit configurations.
AutoPPA introduces a fully automated framework that generates optimization rules automatically rather than relying on manual expertise. The core methodology is an Explore-Evaluate-Induce (E²I) workflow that generates diverse code pairs, evaluates their PPA characteristics, and then contrasts and abstracts optimization patterns from successful transformations to create a rule library. Instead of using predefined human knowledge, the system learns generalizable optimization rules by analyzing what structural changes lead to PPA improvements across different code examples. To enhance applicability across various circuits, AutoPPA implements an adaptive multi-step search framework that dynamically selects and applies the most effective rules for each specific circuit being optimized, allowing the system to tailor its optimization strategy to the particular characteristics of the design at hand.
What the paper shows.
AutoPPA demonstrates superior performance compared to both manual optimization approaches and state-of-the-art automated methods. In experimental evaluations, AutoPPA outperforms SymRTLO and RTLRewriter, which represent the current leading automated PPA optimization techniques. The framework successfully optimizes circuits across performance, power, and area metrics, validating that automatically learned rules can exceed the effectiveness of both human-designed optimizations and existing LLM-based approaches that rely on either pure exploration or manually-defined knowledge bases.
The paper does not explicitly discuss limitations in the abstract. Potential implicit limitations include the computational cost of the Explore-Evaluate-Induce workflow which must generate and evaluate multiple code pairs to learn rules, possible challenges in generalizing learned rules to circuit types significantly different from the training examples, and the dependency on the quality of the initial code generation and evaluation mechanisms. The adaptive multi-step search framework may also require careful tuning to balance exploration of new rules versus exploitation of known effective patterns.
✨ Generated by Claude · Apr 21, 2026 · Read the PDF for authoritative content.