Selected

IC3-Evolve: Proof-/Witness-Gated Offline LLM-Driven Heuristic Evolution for IC3 Hardware Model Checking

IC3, also known as property-directed reachability (PDR), is a commonly-used algorithm for hardware safety model checking. It checks if a state transition system complies with a given safety property. IC3 either returns UNSAFE (indicating property …

A-IC3: Learning-Guided Adaptive Inductive Generalization for Hardware Model Checking

The IC3 algorithm represents the state-of-the-art (SOTA) hardware model checking technique, owing to its robust performance and scalability. A significant body of research has focused on enhancing the solving efficiency of the IC3 algorithm, with …

LeGend: A Data-Driven Framework for Lemma Generation in Hardware Model Checking

Property checking of RTL designs is a central task in formal verification. Among available engines, IC3/PDR is a widely used backbone whose performance critically depends on inductive generalization, the step that generalizes a concrete …

AutoPDR: Circuit-Aware Solver Configuration Prediction for Hardware Model Checking

Property Directed Reachability (PDR) is a powerful algorithm for formal verification of hardware and software systems, but its performance is highly sensitive to parameter configurations. Manual parameter tuning is time-consuming and requires domain …

SegSEM: Enabling and Enhancing SAM2 for SEM Contour Extraction

Extracting high-fidelity 2D contours from Scanning Electron Microscope (SEM) images is critical for calibrating Optical Proximity Correction (OPC) models. While foundation models like Segment Anything 2 (SAM2) are promising, adapting them to …

EvolveGen: Algorithmic Level Hardware Model Checking Benchmark Generation through Reinforcement Learning

Progress in hardware model checking depends critically on high-quality benchmarks. However, the community faces a significant benchmark gap: existing suites are limited in number, often distributed only in representations such as BTOR2 without access …

BDD2Seq: Enabling Scalable Reversible-Circuit Synthesis via Graph-to-Sequence Learning

Binary Decision Diagrams (BDDs) are instrumental in many electronic design automation (EDA) tasks thanks to their compact representation of Boolean functions. In BDD-based reversible-circuit synthesis, which is critical for quantum computing, the …

E-morphic: Scalable Equality Saturation for Structural Exploration in Logic Synthesis

In technology mapping, the quality of the final implementation heavily relies on the circuit structure after technology-independent optimization. Recent studies have introduced equality saturation as a novel optimization approach. However, its …

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

Logic synthesis plays a crucial role in the digital design flow. It has a decisive influence on the final Quality of Results (QoR) of the circuit implementations. However, existing multi-level logic optimization algorithms often employ greedy …

DeepIC3: Guiding IC3 Algorithms by Graph Neural Network Clause Prediction

In recent years, machine learning has demonstrated its potential in many challenging problems. In this paper, we extend its use to hardware formal property verification and propose DeepIC3, a method that takes advantage of graph learning in the …