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arxiv: 2604.22285 · v1 · submitted 2026-04-24 · 💻 cs.AR · cs.SE

AutoINV: Automated Invariant Generation Framework for Formal Verification on High-Level Synthesis Designs

Xiaofeng Zhou , Linfeng Du , Guangyu Hu , Sharad Sinha , Hongce Zhang , Wei Zhang This is my paper

Pith reviewed 2026-05-08 09:34 UTC · model grok-4.3

classification 💻 cs.AR cs.SE
keywords high-level synthesisformal verificationmodel checkinginvariant generationhelper assertionsRTL designsautomated verificationhardware security
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The pith

AutoINV generates helper assertions from high-level synthesis features and iteratively selects the most useful ones to accelerate model checking of generated RTL designs.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces a framework called AutoINV that extracts design features from the high-level synthesis process to automatically build helper assertions. These assertions guide a model checker toward quicker conclusions on functional and security properties of the resulting RTL hardware. A proving mechanism reuses prior results across iterations to identify the subset of helpers that most effectively prune the search space. On evaluated HLS benchmarks this yields verification speedups reaching 6.05 times and averaging 2.23 times compared with running the model checker without assistance. The method targets the practical bottleneck that large machine-generated RTL designs often exceed the time or memory limits of unaided formal verification.

Core claim

AutoINV constructs helper assertions directly from high-level features present in the original algorithmic description before synthesis occurs. It then applies an iterative proving process that reuses information from previous proof attempts to rank and retain only the most effective helpers. When supplied to a standard model checker, the selected helpers reduce the effort required to verify the corresponding RTL implementation without altering the underlying checker or the semantics of the properties being checked.

What carries the argument

Automated construction of helper assertions from HLS design features together with an iterative proving mechanism that reuses prior results to select the most useful subset.

If this is right

  • Model checkers can complete verification on larger HLS-generated designs that previously exceeded time or memory limits.
  • The same RTL netlist can be checked for more properties within a fixed verification budget.
  • Security and functional validation of HLS outputs becomes feasible earlier in the design flow.
  • Development iterations shorten because formal checks finish faster without requiring manual invariant writing.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar feature-driven helper generation could be applied to other synthesis or compilation stages to improve verification at each abstraction level.
  • The iterative selection loop might be reused in software model checking when high-level source annotations are available.
  • Integration inside HLS compilers could enable continuous formal checks during design-space exploration rather than as a post-synthesis step.

Load-bearing premise

The assertions derived from high-level features remain sound with respect to the RTL implementation and do not cause the model checker to miss real bugs or report false correctness.

What would settle it

A benchmark RTL design containing a known functional or security bug where AutoINV either reports the design as correct or produces a longer runtime than vanilla model checking.

Figures

Figures reproduced from arXiv: 2604.22285 by Guangyu Hu, Hongce Zhang, Linfeng Du, Sharad Sinha, Wei Zhang, Xiaofeng Zhou.

Figure 1
Figure 1. Figure 1: Comparison of finding an inductive invariant without view at source ↗
Figure 2
Figure 2. Figure 2: Comparison of verification performance with and view at source ↗
Figure 4
Figure 4. Figure 4: A simplified diagram of pipeline behavior in HLS view at source ↗
Figure 5
Figure 5. Figure 5: Execution order reflected in hardware implementation. view at source ↗
Figure 6
Figure 6. Figure 6: Performance degradation in naive clause side-loading. Helper: h = f(vv) Score 35 view at source ↗
Figure 7
Figure 7. Figure 7: The workflow of AutoINV Helper Ranker. In the bottom left of view at source ↗
read the original abstract

High-level synthesis (HLS) transforms an algorithmic description of hardware from a higher abstraction (e.g., C/C++) into a register-transfer level (RTL) design, offering reduced development time and greater flexibility in design space exploration. However, such machine-generated RTL designs may contain major functional bugs or security vulnerabilities due to limitations or errors in the HLS tools. One of the most reliable methods to identify these vulnerabilities is formal verification, particularly model checking. Nevertheless, the large size of the generated RTL often causes model checking to struggle to conclude within reasonable time or resource limits. In this study, we propose utilizing the high-level design features from the HLS flow to construct a set of helper assertions aimed at guiding the model checker and accelerating the verification process. To identify the most effective set of helpers to assist the model checker, we develop a proving mechanism that iteratively reuses proving information to select the potentially most useful set of helpers. We evaluate the proposed framework on a set of HLS design benchmarks. Experimental results demonstrate that, when compared to vanilla model checking, our approach achieves a speedup of up to 6.05x, and 2.23x on average.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper presents AutoINV, a framework that automatically generates helper assertions (invariants) from high-level features of HLS designs (e.g., C/C++ descriptions) to guide model checking and accelerate formal verification of the resulting RTL implementations. It introduces an iterative proving mechanism that reuses prior verification information to select the most useful subset of helpers. Evaluation on HLS benchmarks reports speedups of up to 6.05x (2.23x average) relative to vanilla model checking.

Significance. If the helper assertions can be shown to be sound (i.e., they preserve verification semantics without introducing false negatives) and the speedups prove reproducible across a broader set of designs, the work would meaningfully improve the scalability of formal methods for HLS-generated hardware. Exploiting high-level information for invariant generation is a promising direction that could reduce the verification bottleneck for complex RTL.

major comments (2)
  1. [Abstract and methodology description] The central speedup claim (Abstract) rests on the assumption that HLS-derived helper assertions are sound lemmas that do not alter the verification outcome. No soundness theorem, inductive argument, or proof sketch is provided showing that the constructed assertions are valid over-approximations or that any counterexample found remains valid for the original design. This is load-bearing: without it, reported gains could stem from over-constraining the state space or missing bugs that vanilla model checking would detect.
  2. [§5 (Experimental Results)] §5 (Experimental Results): the reported speedups lack supporting details on benchmark selection, the number and characteristics of HLS designs, the model checker configuration, timeout thresholds, or statistical error analysis. The 2.23x average and 6.05x maximum cannot be assessed for robustness or generality without this information.
minor comments (2)
  1. [Methodology] The iterative selection algorithm would benefit from pseudocode or a formal description of the reuse of proving information to improve clarity and reproducibility.
  2. [Figures and Tables] Figure captions and table headers should explicitly state the model checker used and the property types verified to allow direct comparison with related work.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. The comments highlight important aspects of soundness and experimental rigor that we will address in the revision.

read point-by-point responses

  1. Referee: [Abstract and methodology description] The central speedup claim (Abstract) rests on the assumption that HLS-derived helper assertions are sound lemmas that do not alter the verification outcome. No soundness theorem, inductive argument, or proof sketch is provided showing that the constructed assertions are valid over-approximations or that any counterexample found remains valid for the original design. This is load-bearing: without it, reported gains could stem from over-constraining the state space or missing bugs that vanilla model checking would detect.

    Authors: We agree that an explicit soundness argument is essential to support the speedup claims and rule out over-constraining or missed bugs. The helper assertions are constructed from high-level C/C++ features (such as loop bounds and data dependencies) that are semantically preserved by the HLS translation to RTL. We will add a dedicated subsection in the methodology section providing a proof sketch: the assertions are valid invariants at the high level and thus hold in the generated RTL (by HLS semantics preservation), making them sound over-approximations. This ensures counterexamples remain valid for the original design. The iterative selection mechanism only adds helpers that are proven or consistent with prior checks. revision: yes

  2. Referee: [§5 (Experimental Results)] §5 (Experimental Results): the reported speedups lack supporting details on benchmark selection, the number and characteristics of HLS designs, the model checker configuration, timeout thresholds, or statistical error analysis. The 2.23x average and 6.05x maximum cannot be assessed for robustness or generality without this information.

    Authors: We acknowledge that the current description of the experimental setup in §5 is insufficient for full reproducibility and assessment. In the revised manuscript, we will expand §5 with: (1) explicit benchmark selection criteria and sources, (2) the exact number of HLS designs along with their characteristics (e.g., lines of code, loop structures, bit-widths), (3) the specific model checker, version, and configuration parameters used, (4) timeout thresholds (e.g., per-instance limits), and (5) any statistical measures such as variance or multiple-run analysis. This will allow readers to better evaluate the robustness of the 2.23x average and 6.05x maximum speedups. revision: yes

Circularity Check

0 steps flagged

No significant circularity; results are externally benchmarked

full rationale

The paper describes an engineering framework that extracts helper assertions from HLS design features and applies an iterative selection process using proving information to accelerate model checking. The central claim is an empirical speedup (up to 6.05x, 2.23x average) measured against vanilla model checking on a set of HLS benchmarks. No equations, fitted parameters, or self-referential definitions appear in the provided text that would reduce any result to its own inputs by construction. The evaluation relies on external model-checking tools and independent benchmark designs rather than internal self-citation chains or ansatzes smuggled via prior work. This is a standard experimental validation setup with no load-bearing circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that high-level HLS features yield useful, sound helper assertions; no free parameters or invented entities are explicitly introduced in the abstract.

axioms (1)
  • domain assumption High-level design features from the HLS flow can be used to construct a set of helper assertions that guide the model checker and accelerate verification without altering correctness.
    This premise underpins the entire proposed framework and proving mechanism described in the abstract.

pith-pipeline@v0.9.0 · 5520 in / 1227 out tokens · 26912 ms · 2026-05-08T09:34:06.476800+00:00 · methodology

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