# A New Benchmark for the Appropriate Evaluation of RTL Code Optimization

arXiv: https://arxiv.org/abs/2601.01765 · HTML: https://mlsanity.com/paper/2601.01765/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jan 2026.

## Built on by (most cited first, 5 of 5)

- [Exploring the Agentic Frontier of Verilog Code Generation](https://mlsanity.com/paper/2603.19347/) (2026)
- [ARES: Adaptive Reasoning-Effort Steering for PPA- and Cost-Aware RTL Optimization with LLM Agents](https://mlsanity.com/paper/2607.27879/) (2026)
- [TicTacBench: Benchmarking Timing Closure Capabilities of Coding Agents](https://mlsanity.com/paper/2609.23363/) (2026)
- [ChipMEM: Verification-Grounded Memory for EDA Agents](https://mlsanity.com/paper/2609.27067/) (2026)
- [BEHAVE: Functional Behavior Modeling Enables Self-Improving Agents for Hardware Design and Verification](https://mlsanity.com/paper/2609.34785/) (2026)

## Abstract (arXiv)

The rapid progress of artificial intelligence increasingly relies on efficient integrated circuit (IC) design. Recent studies have explored the use of large language models (LLMs) for generating Register Transfer Level (RTL) code, but existing benchmarks mainly evaluate syntactic correctness rather than optimization quality in terms of power, performance, and area (PPA). This work introduces RTL-OPT, a benchmark for assessing the capability of LLMs in RTL optimization. RTL-OPT contains 36 handcrafted digital designs that cover diverse implementation categories including combinational logic, pipelined datapaths, finite state machines, and memory interfaces. Each task provides a pair of RTL codes, a suboptimal version and a human-optimized reference that reflects industry-proven optimization patterns not captured by conventional synthesis tools. Furthermore, RTL-OPT integrates an automated evaluation framework to verify functional correctness and quantify PPA improvements, enabling standardized and meaningful assessment of generative models for hardware design optimization.