arXiv subjects

Yang Yin

Publications and source records attributed to Yang Yin.

3 recordsLinked to original sources

arXiv2026

CoverAssert: Iterative LLM Assertion Generation Driven by Functional Coverage via Syntax-Semantic Representations

LLMs can generate SystemVerilog assertions (SVAs) from natural language specs, but single-pass outputs often lack functional coverage due to limited IC design understanding. We propose CoverAssert, an iterative framework that clusters semantic and AST-based structural features of assertions, maps them to specifications, and uses functional coverage feedback to guide LLMs in prioritizing uncovered points. Experiments on four open-source designs show that integrating CoverAssert with AssertLLM and Spec2Assertion improves average improvements of 9.57 % in branch coverage, 9.64 % in statement coverage, and 15.69 % in toggle coverage.

cs.AR↗

arXiv2026

Iterative LLM-Based Assertion Generation Using Syntax-Semantic Representations for Functional Coverage-Guided Verification

While leveraging LLMs to automatically generate SystemVerilog assertions (SVAs) from natural language specifications holds great potential, existing techniques face a key challenge: LLMs often lack sufficient understanding of IC design, leading to poor assertion quality in a single pass. Therefore, verifying whether the generated assertions effectively cover the functional specifications and designing feedback mechanisms based on this coverage remain significant hurdles. To address these limitations, this paper introduces CoverAssert, a novel iterative framework for optimizing SVA generation with LLMs. The core contribution is a lightweight mechanism for matching generated assertions with specific functional descriptions in the specifications. CoverAssert achieves this by clustering the joint representations of semantic features of LLM-generated assertions and structural features extracted from abstract syntax trees (ASTs) about signals related to assertions, and then mapping them back to the specifications to analyze functional coverage quality. Leveraging this capability, CoverAssert constructs a feedback loop based on functional coverage to guide LLMs in prioritizing uncovered functional points, thereby iteratively improving assertion quality. Experimental evaluations on four open-source designs demonstrate that integrating CoverAssert with state-of-the-art generators, AssertLLM and Spec2Assertion, achieves average improvements of 9.57 % in branch coverage, 9.64 % in statement coverage, and 15.69 % in toggle coverage.

cs.AR↗

arXiv2021

High-order Differentiable Autoencoder for Nonlinear Model Reduction

This paper provides a new avenue for exploiting deep neural networks to improve physics-based simulation. Specifically, we integrate the classic Lagrangian mechanics with a deep autoencoder to accelerate elastic simulation of deformable solids. Due to the inertia effect, the dynamic equilibrium cannot be established without evaluating the second-order derivatives of the deep autoencoder network. This is beyond the capability of off-the-shelf automatic differentiation packages and algorithms, which mainly focus on the gradient evaluation. Solving the nonlinear force equilibrium is even more challenging if the standard Newton's method is to be used. This is because we need to compute a third-order derivative of the network to obtain the variational Hessian. We attack those difficulties by exploiting complex-step finite difference, coupled with reverse automatic differentiation. This strategy allows us to enjoy the convenience and accuracy of complex-step finite difference and in the meantime, to deploy complex-value perturbations as collectively as possible to save excessive network passes. With a GPU-based implementation, we are able to wield deep autoencoders (e.g., $10+$ layers) with a relatively high-dimension latent space in real-time. Along this pipeline, we also design a sampling network and a weighting network to enable \emph{weight-varying} Cubature integration in order to incorporate nonlinearity in the model reduction. We believe this work will inspire and benefit future research efforts in nonlinearly reduced physical simulation problems.

cs.LG↗