The increasing complexity of integrated circuit design requires customizing Power, Performance, and Area (PPA) metrics according to different application demands. However, most engineers cannot anticipate requirements early in the design process, often discovering mismatches only after synthesis, necessitating iterative optimization or redesign. Some works have shown the promising capabilities of large language models (LLMs) in hardware design generation tasks, but they fail to tackle the PPA trade-off problem. In this work, we propose an LLM-based reinforcement learning framework, PPA-RTL, aiming to introduce LLMs as a cutting-edge automation tool by directly incorporating post-synthesis metrics PPA into the hardware design generation phase. We design PPA metrics as reward feedback to guide the model in producing designs aligned with specific optimization objectives across various scenarios. The experimental results demonstrate that PPARTL models, optimized for Power, Performance, Area, or their various combinations, significantly improve in achieving the desired trade-offs, making PPA-RTL applicable to a variety of application scenarios and project constraints.

Recent advances in large language models have significantly improved the capabilities of programming. While these models excel at generating valid software, applying them to the hardware domain remains challenging due to the intrinsic complexity and strict structural semantics required in hardware design. Current LLM approaches for hardware generation typically focus on direct generation. This often results in hardware implementations with functional errors or structural flaws. To overcome these limitations, we propose a reasoning-enhanced training framework explicitly tailored for hardware generation tasks. Our multi-stage methodology combines systematic dataset curation via compilation filtering (achieving a 100% pass rate compared to 27 − 44% in existing datasets), Function-Aligned Differentiated Revision for comparative annotation across five RTL-relevant dimensions, supervised fine-tuning using reasoning prompts, and reinforcement learning guided by Verilator Parser. Our experiments show that explicitly incorporating reasoning substantially enhances the structural integrity and functional correctness of generated hardware designs, improving pass@1 rates by up to 20% on VerilogEval Human benchmarks and reproducing the "Aha moment", where the model explicitly organizes ideas before generation. Our work demonstrates that smaller, specialized reasoning models (1.5B parameters) can effectively augment larger open-source language models through reasoning transfer.

Integrated circuit design is a highly complex and time-consuming process. Leveraging large language models (LLMs) for automating hardware design generation is receiving increasing attention. A prominent challenge is that the inherent structure of the text is overlooked during the training process. Existing efforts focus on supervised fine-tuning LLMs to acquire specialized knowledge in hardware design, without considering the conflict between LLMs’ linear data processing and the structural nature inherent in hardware design. In this work, we propose a novel LLM-based reinforcement learning (RL) framework that integrates Abstract Syntax Trees (ASTs) and Data Flow Graphs (DFGs). Our approach enhances the accuracy of generated hardware code by capturing the syntactic and semantic structures of hardware designs. Experimental results show that the SFT-RL model integrated with Text, AST, and DFG achieves notable improvements: a 12.57% increase on VerilogEval-Human and a 5.49% increase on VerilogEval-Machine, outperforming GPT-4; a 14.29% improvement on RTLLM, approaching GPT-4.

Large Language Models (LLMs) have precipitated emerging trends towards intelligent automation. However, integrating LLMs into the hardware debug domain encounters challenges: the datasets for LLMs for hardware are often plagued by a dual dilemma – scarcity and subpar quality. Traditional hardware debug approaches that rely on experienced labor to generate detailed prompts are not cheaply scalable. Similarly, strategies that depend on existing LLMs and randomly generated prompts fail to achieve sufficient reliability. We propose a directed, semi-synthetic data synthetic method that leverages version control information and journalistic event descriptions. To produce high-quality data, this approach utilizes version control data from hardware projects combined with the 5W1H (Who, What, When, Where, Why, How) journalistic principles. It facilitates the linear scaling of dataset volumes without depending on skilled labor. We have implemented this method on a collected dataset of open-source hardware designs and fine-tuned fifteen general-purpose LLMs to enable their capability in hardware debugging tasks, thereby validating the efficacy of our approach.

In the rapidly evolving semiconductor industry, where research, design, verification, and manufacturing are intricately linked, the potential of Large Language Models to revolutionize hardware design and security verification is immense. The primary challenge, however, lies in the complexity of hardware-specific issues that are not adequately addressed by the natural language or software code knowledge typically acquired during the pretraining stage. Additionally, the scarcity of datasets specific to the hardware domain poses a significant hurdle in developing a foundational model. Addressing these challenges, this paper introduces Hardware Phi-1.5B, an innovative large language model specifically tailored for the hardware domain of the semiconductor industry. We have developed a specialized, tiered dataset—comprising small, medium, and large subsets—and focused our efforts on pretraining using the medium dataset. This approach harnesses the compact yet efficient architecture of the Phi-1.5B model. The creation of this first pre-trained, hardware domain-specific large language model marks a significant advancement, offering improved performance in hardware design and verification tasks and illustrating a promising path forward for AI applications in the semiconductor sector.