Skip to content
Review

ChipVerilog: A Large-Scale OpenCores-Derived Benchmark for LLM-Based Verilog RTL Generation

Jul 2026 · arXiv.org · Vol abs/2607.13079 · 1 citation · 34 references
Computer Science

TL;DR

ChipVerilog is presented, a description-to-Verilog generation benchmark built from OpenCores IP/core designs that shows that large-scale RTL remains challenging, especially for hierarchical and cross-module designs.

Abstract

Large language models have shown strong potential for Verilog RTL generation. However, many existing benchmarks are built from short, self-contained module-level tasks. These tasks are useful for controlled evaluation, but they do not fully capture the code scale, hierarchy, and module interactions found in practical IP and processor-core RTL. We present ChipVerilog, a description-to-Verilog generation benchmark built from OpenCores IP/core designs. The benchmark contains 64 generation targets from five design families: OR1200, double-precision FPU, MIPS-16, I2C, and CORDIC. It includes both single-module targets and cross-module targets that instantiate or interact with other RTL modules. Several targets exceed 1,000 lines of Verilog, making ChipVerilog substantially larger and structurally more complex than typical module-level suites. Each benchmark instance is constructed from a pair of specification documents and reference RTL. We extract the target functionality, write a detailed natural-language description, and manually review the description for correctness and clarity. Generated RTL is checked by compilation and validated through equivalence checking for local modules, or by simulation for integrated IP/core targets. Results show that large-scale RTL remains challenging, especially for hierarchical and cross-module designs.

View source

Similar papers

Preprint Aug 2026

NoTB: Oracle-Free Triage of LLM-Generated RTL via Cross-Model Formal Consensus

NoTB is introduced, an oracle-free triage framework that infers correctness from cross-model formal consensus and demonstrates that formal cross-model agreement provides a reliable basis for high-confidence triage without model-dependent oracles.

Elisavet Lydia Alvanaki, Je Yang, Biruk B. Seyoum et al. · 0 citations

Automated Generation of RISC-V Extensions with Formal Correctness Guarantees

Janus is presented, an LLM-assisted framework that synthe-sizes custom instructions integrated into the Ibex RISC-V core while keeping correctness outside the agent, demonstrating a practical path for using LLMs to explore ISA specialization without making the agent part of the trusted correctness boundary.

Elisavet Lydia Alvanaki, Jia-Kun Wang, Eugenio Muscinelli et al. · 0 citations
#machine learning Review Sep 2026

Robustness of LLM-Generated SystemVerilog Assertions to Semantics-Preserving RTL Transformations

Large language models (LLMs) are increasingly being explored for automating SystemVerilog Assertion (SVA) generation, yet most evaluations report correctness on a single syntactic representation of an input. Such point accuracy does not reveal whether a model's correct output is stable when the same RTL behavior is wri...

Fnu Aditi · 1 citation
Preprint Sep 2026

SpecLens: LLM-Based Verilog Generation with Specification-Derived Constraints via Behavioral Divergence

Large language models (LLMs) have recently shown promise in Verilog generation, but producing functionally correct RTL directly from natural-language specifications remains a highly challenging task. Existing approaches improve LLM-based Verilog generation mainly with retrieval-augmented generation (RAG), self-planning...

Wen Bing, Bing Li · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.