AI

Coverage-Driven RTL Assertion Generation with Formal Exploration and Neuro-Symbolic Refinement

Researchers have developed a new system for generating high-quality assertions in hardware design. Assertions are like checks to ensure that digital designs behave as expected. The current method of creating these assertions is limited and can miss critical behaviors. The new system, called NeuroAssertion, uses a combination of formal trace generation, syntax-guided synthesis, and agent-inspired refinement to create more comprehensive assertion sets. This results in around 2
Researchers have developed a new system for generating high-quality assertions in hardware design. Assertions are like checks to ensure that digital designs behave as expected. The current method of creating these assertions is limited and can miss critical behaviors. The new system, called NeuroAssertion, uses a combination of formal trace generation, syntax-guided synthesis, and agent-inspired refinement to create more comprehensive assertion sets. This results in around 2 times more assertions and higher coverage than traditional methods. The authors claim this improvement comes from the use of two large language models (LLMs) that work together to refine and repair assertions. --- Why it matters: This matters because it can help engineers and researchers improve the reliability and efficiency of hardware design verification, which is a critical step in developing complex digital systems. Source: https://arxiv.org/abs/2608.18482

This article was originally published at: https://arxiv.org/abs/2608.18482