E-Syn: E-Graph Rewriting for Logic Synthesis with Technology-Aware Cost Functions

Core Concepts

E-Syn proposes using e-graph rewriting in logic synthesis to explore a wider design space and incorporate technology-aware cost functions for better optimization.

Abstract

The paper introduces E-Syn, a novel logic optimization method that leverages e-graphs for combinational logic rewriting. It addresses the limitations of existing approaches by exploring a diverse set of equivalent Boolean representations and allowing technology-aware cost functions. The proposed method achieves significant improvements in delay-oriented and area-oriented synthesis compared to traditional AIG-based logic synthesis flows. The paper also presents a detailed framework of E-Syn, including rewriting rules, extraction methods, regression models, and integration with existing synthesis flows. Experimental results demonstrate the effectiveness of E-Syn in reaching a wider design space and outperforming traditional logic synthesis approaches.
Abstract:

Logic synthesis is crucial in digital design flow.
Existing multi-level logic optimization algorithms have limitations.
E-Syn proposes using e-graphs for efficient logic rewriting.
Achieves significant improvements in delay and area optimizations.
Introduction:

Logic synthesis influences Quality of Results (QoR).
Technology-independent optimization followed by mapping.
ABC framework commonly used for And-Inverter Graph (AIG) representation.
Challenges include local greedy decisions and technology-independent metrics.
E-Graph Rewriting for Logic Synthesis:

Utilizes e-graph to represent equivalent Boolean functions efficiently.
Clusters equivalent classes into e-classes for compact representation.
Uses equality saturation technique with Boolean algebra rules for rewriting.
Extraction:

Traverses e-graph to select optimal logic form based on cost model.
Regression model predicts area and delay costs from AST features.
Experiment Result and Discussion:

Pool extraction method improves efficiency using technology-aware cost models.
Comparison shows E-Syn outperforms ABC flow in delay, area, and balanced optimizations.
Conclusion:
E-Syn extends e-graph optimization to bit-level logic rewriting, achieving wider design space exploration with technology-aware cost functions.

Stats

Experiments show our proposed approach achieves on average 15.29% delay saving in delay-oriented synthesis and 6.42% area saving for area-oriented synthesis.

Quotes

Key Insights Distilled From

E-Syn

by Chen Chen,Gu... at arxiv.org 03-22-2024

https://arxiv.org/pdf/2403.14242.pdf

Deeper Inquiries

How can the use of e-graphs impact other areas of digital design beyond logic synthesis?

E-graphs have the potential to revolutionize various aspects of digital design beyond logic synthesis. One significant area where e-graphs can make a substantial impact is in high-level synthesis (HLS). By leveraging e-graph optimization techniques, HLS tools can explore a wider range of equivalent representations at higher abstraction levels, leading to more efficient hardware implementations. E-graphs can help in optimizing complex datapaths and control structures, improving performance and reducing resource utilization in FPGA or ASIC designs.
Moreover, e-graphs can be applied to formal verification processes. By representing equivalence classes efficiently, e-graph-based methods can enhance equivalence checking algorithms for verifying RTL designs against specifications or between different implementation stages. This application could lead to faster and more accurate verification results by exploring diverse logical equivalences systematically.
Additionally, in physical design automation tasks such as floorplanning and placement-and-routing, e-graph techniques could aid in optimizing layout configurations based on logical equivalences within the circuit. This approach may result in better area utilization, reduced wirelengths, improved timing closure, and overall enhanced chip performance.

How might the application of machine learning models enhance the capabilities of e-graph-based optimizations?

The integration of machine learning models with e-graph-based optimizations presents several opportunities for enhancing design exploration and achieving superior quality-of-results (QoR) in digital design tasks:

Customizable Cost Functions: Machine learning models enable the creation of technology-aware cost functions that capture intricate relationships between different optimization objectives like delay reduction or area minimization. These cost functions guide the selection process during extraction from an e-class pool towards optimal solutions tailored to specific requirements.

Regression Models for QoR Prediction: By training regression models on features extracted from AST nodes or graph properties derived from circuits' characteristics post-mapping QoR metrics like delay or area consumption are predicted accurately using XGBoost regression model which bridges technology-independent logic costs with actual post-mapping QoRs effectively guiding global decision-making during extraction steps.

Optimization Targeting Specific Technologies: Machine learning enables adaptive optimization strategies that learn patterns from past designs under specific technologies allowing designers to fine-tune their approaches based on historical data resulting in optimized solutions aligned with target technologies ensuring better compatibility with manufacturing constraints.

What potential drawbacks or criticisms could be raised against the methodology proposed by E-Syn?

While E-Syn introduces innovative concepts into logic synthesis through its use of e-graph rewriting with technology-aware cost functions there are some potential drawbacks or criticisms that could be considered:

Complexity Overhead: The incorporation of advanced methodologies like equality saturation via e-classes coupled with machine learning-driven cost modeling may introduce additional complexity into existing design flows requiring specialized expertise for implementation maintenance potentially increasing development time and effort.

Resource Intensiveness: The computational resources required for running equality saturation algorithms along with XGBoost regression models especially on large-scale circuits might pose challenges regarding runtime efficiency scalability particularly when dealing with extensive datasets necessitating robust infrastructure support.

Overfitting Concerns: There is a risk associated with overfitting when training regression models on limited datasets which might not fully represent all possible scenarios leading to suboptimal generalization across diverse circuit designs impacting prediction accuracy negatively affecting final QoR outcomes.

4 .Interpretability Challenges: While machine learning enhances predictive capabilities it also brings about interpretability challenges making it difficult for designers to comprehend how decisions are made during extraction steps raising concerns about transparency accountability especially when dealing with safety-critical applications where explainability is crucial.