VeriOpt: PPA-Aware RTL Generation

• VeriOpt is a multi-role framework that uses LLM prompting and synthesis feedback to generate synthesizable Verilog optimized for power, performance, and area.
• The framework splits RTL generation into Planner, Programmer, Reviewer, and Evaluator roles, ensuring systematic design verification and traceability.
• Experimental results show up to 88% reduction in power, 76% reduction in area, and 73% improvement in timing, demonstrating its industrial viability.

Searching arXiv for papers directly relevant to "VeriOpt" and closely related verification-based optimization/modeling systems. VeriOpt is a framework for generating industrial-quality Verilog/RTL with LLMs that is both functionally correct and optimized for Power, Performance, Area (PPA) (Tasnia et al., 20 Jul 2025). In the cited literature, the name also appears more broadly as a design motif for verification-centric systems: optimization-model verification via dual-side semantic checks (Liu et al., 28 May 2026), confidence estimation for verification-based LLM judges (Qi et al., 11 May 2026), worst-case optimality-gap certification for optimization proxies (Chen et al., 2024), and execution-verified reinforcement learning for optimization modeling (Guan et al., 1 Apr 2026). In its primary, paper-specific sense, however, VeriOpt denotes a multi-role, PPA-aware prompting framework for synthesizable Verilog generation that couples role-specialized LLM interaction with external EDA feedback, using synthesis reports and timing-oriented signals to improve implementation quality rather than merely functional correctness (Tasnia et al., 20 Jul 2025).

1. Definition and scope

VeriOpt addresses a specific deficiency in LLM-based RTL generation: prior systems often target syntactic correctness and simulation-based functional correctness while largely ignoring PPA (Tasnia et al., 20 Jul 2025). The framework is designed to produce synthesizable Verilog that is both correct and PPA-efficient, aligned with industrial constraints, and it does so without fine-tuning, using only prompting and external EDA tools (Tasnia et al., 20 Jul 2025).

The motivating distinction is between code that passes a testbench and hardware that satisfies downstream implementation constraints. The paper explicitly argues that functionally correct RTL may still be power-hungry, large in area, or unable to meet timing, and therefore may fail at the level of timing closure or product viability (Tasnia et al., 20 Jul 2025). PPA is treated as a first-class optimization target at RTL, not as a downstream afterthought.

The framework is centered on the standard PPA triad. Power comprises dynamic and leakage components; performance is expressed through quantities such as critical path delay, maximum clock frequency, and timing slack; area refers to cell area and design area (Tasnia et al., 20 Jul 2025). Timing closure is defined through the relation

$\text{Slack} = T - \text{Critical Path Delay},$

where negative slack implies a timing violation (Tasnia et al., 20 Jul 2025).

2. Architecture and multi-role workflow

VeriOpt structures LLM interactions into four roles: Planner, Programmer, Reviewer, and Evaluator (Tasnia et al., 20 Jul 2025). This decomposition is intended to emulate human RTL design workflows, with plan construction, implementation, review, and verification separated into specialized prompting stages.

The high-level workflow begins with a natural-language specification and module header, which are passed to the Planner (Tasnia et al., 20 Jul 2025). The Planner outputs a numbered list of implementation steps, such as defining internal registers and parameters, FSM states and encoding, the number and type of always blocks, and assignment style (Tasnia et al., 20 Jul 2025). These steps are intended to be synthesizable, hardware-realistic, and granular enough for later traceability.

The Programmer then converts the plan into Verilog code, explicitly mapping each implementation step to code segments and annotating the code with comments that preserve this alignment (Tasnia et al., 20 Jul 2025). The Reviewer compares the code against the step list and reports whether each planned element has been implemented and where it appears (Tasnia et al., 20 Jul 2025). The Evaluator receives the generated code, the RTLLM benchmark testbench, and any syntax or functional error logs from Icarus Verilog, then diagnoses the failures and proposes concrete repairs (Tasnia et al., 20 Jul 2025).

This process forms an iterative correction loop. The Programmer applies fixes, the Reviewer checks that the requested fixes are present, and the Evaluator re-runs verification until the design passes the testbench (Tasnia et al., 20 Jul 2025). The result is a functionally validated baseline RTL prior to PPA optimization.

Role	Input	Output
Planner	Natural-language spec and module declaration	Numbered implementation steps
Programmer	Spec and plan	Verilog RTL with step-aligned comments
Reviewer	Plan and generated code	Step-by-step implementation audit
Evaluator	Code, testbench, error logs	Diagnoses and concrete fixes

A plausible implication is that this role partition reduces failure entanglement: plan incompleteness, implementation mistakes, and verification errors are surfaced in different artifacts rather than collapsed into a single monolithic generation step.

3. PPA-aware optimization mechanism

After functional correctness is established, VeriOpt enters a PPA-aware optimization phase driven by in-context learning and external synthesis feedback (Tasnia et al., 20 Jul 2025). The framework provides the LLM with domain knowledge about power, delay, area, and optimization techniques; example RTL snippets; the baseline RTL; and Design Compiler synthesis reports containing power, area, and timing metrics (Tasnia et al., 20 Jul 2025).

The prompts incorporate textbook-style descriptions of dynamic and leakage power, delay estimation via parasitics, logic depth and critical path, and RTL-level optimization strategies (Tasnia et al., 20 Jul 2025). The strategies explicitly taught in context include clock gating, power gating, operand isolation, register update suppression, conditional accumulation, pipelining, register retiming, loop unrolling, path restructuring, resource sharing, FSM state encoding, logic consolidation, register optimization, and technology-mapping-conscious coding patterns (Tasnia et al., 20 Jul 2025).

Synthesis reports are not treated as mere pass/fail signals. VeriOpt feeds back detailed PPA breakdowns, including dynamic power, leakage power, cell internal versus net switching power, cell area, design area, combinational versus sequential area, critical path length, critical path slack, total negative slack, and levels of logic (Tasnia et al., 20 Jul 2025). The LLM then analyzes both design structure and reported bottlenecks, proposing transformations specialized for power, area, timing, or combinations thereof (Tasnia et al., 20 Jul 2025).

The paper does not define an explicit scalarized objective such as a weighted sum over power, area, and delay (Tasnia et al., 20 Jul 2025). Instead, optimization is goal-conditioned through natural-language targets such as power-optimized, timing-optimized, or area-optimized variants (Tasnia et al., 20 Jul 2025). This suggests that VeriOpt is best understood as an overview-report-guided search over coding transformations expressed through prompting rather than a formal multi-objective optimizer.

4. Code generation, verification, and refinement

The initial RTL produced by VeriOpt is constrained to use synthesizable constructs, explicit reset handling, and clear separation of combinational and sequential logic (Tasnia et al., 20 Jul 2025). The step-to-code comments are not only documentary; they support downstream review and debugging by preserving a direct alignment between design intent and implementation.

Functional correctness is enforced through simulation-based verification using Icarus Verilog and RTLLM’s golden testbench (Tasnia et al., 20 Jul 2025). A design is deemed functionally passed only if it is syntactically correct and passes all testbench cases (Tasnia et al., 20 Jul 2025). This criterion is used consistently across VeriOpt and baseline methods (Tasnia et al., 20 Jul 2025).

PPA-driven refinement then applies structural and control-logic transformations. The paper lists structural changes such as inserting or deepening pipeline stages, replacing ripple carry adders with faster architectures such as carry look-ahead, adjusting FSM encoding, and introducing resource sharing (Tasnia et al., 20 Jul 2025). Control-oriented refinements include adding clock gating around registers, using operand isolation to reduce unnecessary toggling, and suppressing register updates when outputs need not change (Tasnia et al., 20 Jul 2025).

A concrete example is the 16-bit adder case study. Starting from a functionally correct but PPA-suboptimal adder, the LLM identifies a ripple-adder style and lack of gating, then proposes operand isolation and clock gating for power, carry look-ahead plus pipelining for timing, and resource sharing for area (Tasnia et al., 20 Jul 2025). It then emits specialized power-optimized, timing-optimized, and area-optimized variants, each evaluated through synthesis (Tasnia et al., 20 Jul 2025).

5. Experimental evaluation

VeriOpt is evaluated on the RTLLM benchmark, which contains 29 Verilog designs spanning arithmetic modules, counters, shifters, multiplexers, FSMs, RAM, asynchronous FIFO, ALU, processing element, signal generator, calendar, and width converters (Tasnia et al., 20 Jul 2025). The experiments use GPT-4o for all VeriOpt roles, Icarus Verilog for simulation, and Synopsys Design Compiler for synthesis (Tasnia et al., 20 Jul 2025).

For functional correctness, VeriOpt achieves 25 out of 29 designs passed, approximately 86%, and produces 100% syntactically correct code (Tasnia et al., 20 Jul 2025). Reported comparison points include GPT-3.5 at 10/29, GPT-4 at 15/29, RTLCoder-Mistral-4bit at 12/29, RTLCoder-Mistral at 14/29, RTLLM GPT-3.5 Self-Planning at 14/29, and RTLLM GPT-4 Self-Planning at 19/29 (Tasnia et al., 20 Jul 2025).

For PPA, the paper reports improvements relative to VeriOpt’s own functionally correct baseline RTL (Tasnia et al., 20 Jul 2025). Headline results are up to 88% reduction in power, up to 76% reduction in area, and up to 73% improvement in timing closure, while maintaining the 86% functional success rate (Tasnia et al., 20 Jul 2025).

More specific figures are reported for multiple designs. Dynamic power reduction reaches 87.96% on the JC counter, leakage power reduction reaches 76.87% on the same design, cell area reduction reaches 75.89%, design area reduction reaches 75.84%, and critical path length reduction reaches 73.23% on the 16-bit divider (Tasnia et al., 20 Jul 2025). For the 16-bit adder case study, the paper reports reductions of 58.70% in cell area, 59.11% in design area, 57.03% in dynamic power, 61.21% in leakage power, and 58.40% in critical path length (Tasnia et al., 20 Jul 2025).

Metric	Reported improvement
Functional success	25/29 designs passed (≈86%)
Power	Up to 88% reduction
Area	Up to 76% reduction
Timing closure	Up to 73% improvement

The paper also notes trade-offs. Some designs show reduced dynamic power but slightly increased leakage, and the right shifter shows worse slack due to an initially marginal slack value of $-0.02$ ns, making percentage changes unstable (Tasnia et al., 20 Jul 2025). This is consistent with standard PPA tension rather than indicating monotone improvement in every objective for every module.

6. Position in the literature and related meanings of “VeriOpt”

Within hardware-oriented LLM research, VeriOpt is positioned against systems such as Verigen, RTLCoder, RTLLM, ChipGPT, HiVeGen, BetterV, RTLSquad, MCTS-based approaches, and Mg-Verilog (Tasnia et al., 20 Jul 2025). The paper’s stated novelty is the combination of an explicit multi-role framework tailored to RTL and PPA-aware prompting driven by real synthesis reports rather than proxy metrics (Tasnia et al., 20 Jul 2025).

The broader literature represented in the cited corpus uses “VeriOpt” more generically to denote verification-centered optimization systems. Opt-Verifier introduces dual-side verification for optimization modeling, checking both model structure against distilled problem semantics and solution behavior against the original natural-language narrative (Liu et al., 28 May 2026). EVOM treats a mathematical programming solver as a deterministic interactive verifier in a reinforcement-learning loop for optimization modeling, using execution outcomes as verifiable rewards (Guan et al., 1 Apr 2026). “Compact Optimality Verification for Optimization Proxies” studies exact certification of worst-case optimality gaps for learned optimizers via a compact high-point formulation (Chen et al., 2024). VERDI, although focused on LLM judges rather than optimization solvers, provides a single-call confidence layer for verification-style decisions by extracting structural signals from traces (Qi et al., 11 May 2026).

These are distinct systems, but they share a family resemblance: verification is elevated from a post hoc diagnostic to a primary systems component. This suggests a broader editorial usage of “VeriOpt” as an umbrella for verification-oriented optimization pipelines, though that broader usage is inferential rather than the title of a single unified research program.

7. Limitations and future directions

The VeriOpt paper identifies several limitations. The framework does not provide autonomous tool integration: the LLM does not itself run synthesis, and humans or scripts must invoke Design Compiler and feed reports back into the prompting loop (Tasnia et al., 20 Jul 2025). Context length limits may make it difficult to handle large designs, long iteration histories, or large code bases within a single prompt (Tasnia et al., 20 Jul 2025). PPA-aware behavior also depends on the quality of in-context materials such as textbook snippets and example RTL, which may limit generalization to more complex designs unless additional curated material is supplied (Tasnia et al., 20 Jul 2025).

Future directions named in the paper include fine-tuning on domain-specific RTL and PPA data, retrieval-augmented generation for dynamic access to optimization knowledge, tighter EDA integration for automated synthesis-feedback loops, scaling to larger multi-module designs, and use of multi-modal LLMs that incorporate waveforms or schematic diagrams (Tasnia et al., 20 Jul 2025).

A plausible implication is that the main barrier to scaling VeriOpt is not the prompting scaffold alone but the closed-loop coupling between LLM reasoning and industrial toolchains. If that coupling becomes more automated and more granular, the framework could evolve from guided prompt engineering into a more systematic search-and-verify pipeline over RTL transformations.

In the current literature, VeriOpt therefore denotes both a concrete PPA-aware Verilog-generation framework and, by extension, a broader methodological stance: LLM-generated technical artifacts should be optimized under explicit verification signals tied to the actual quality criteria of the target domain, whether those criteria are timing closure in RTL (Tasnia et al., 20 Jul 2025), semantic faithfulness in optimization modeling (Liu et al., 28 May 2026), calibrated trust in LLM judges (Qi et al., 11 May 2026), certified optimality gaps (Chen et al., 2024), or execution-correct solver code (Guan et al., 1 Apr 2026).