Divergent Thoughts toward One Goal: LLM -based Multi-Agent Collaboration System for Electronic Design Automation (2025)

Abstract: Recently, with the development of tool-calling capabilities in large language models (LLMs), these models have demonstrated significant potential for automating electronic design automation (EDA) flows by interacting with EDA tool APIs via EDA scripts.However, considering the limited understanding of EDA tools, LLMs face challenges in practical scenarios where diverse interfaces of EDA tools exist across different platforms.Additionally, EDA flow automation often involves intricate, long-chain tool-calling processes, increasing the likelihood of errors in intermediate steps.Any errors will lead to the instability and failure of EDA flow automation.To address these challenges, we introduce EDAid, a multi-agent collaboration system where multiple agents harboring divergent thoughts converge towards a common goal, ensuring reliable and successful EDA flow automation. Specifically, each agent is controlled by ChipLlama models, which are expert LLMs fine-tuned for EDA flow automation.Our experiments demonstrate the state-of-the-art (SOTA) performance of our ChipLlama models and validate the effectiveness of our EDAid in the automation of complex EDA flows, showcasing superior performance compared to single-agent systems.

## introduction electronic design automation (eda) is indispensable for the design of integrated circuits (ics). eda tools are integrated into a complex design flow and utilize programming interfaces to control the design process. eda platforms such as open-road @xcite and ieda @xcite , consist of complex procedures with various configurations. circuit design engineers utilize eda tools iteratively to fulfill design targets, relying on tailored scripts that manipulate these tools via programming interfaces. however, interacting with eda tools through scripting @xcite is often laborious and error-prone. this complexity is further intensified when design teams employ tools from various vendors in the circuit design process. large language models (llms) @xcite @xcite have demonstrated profound instruction comprehension, planning, and reasoning capabilities. the potential of llms to interact with diverse tools for executing complex tasks has gained increasing recognition @xcite . researchers have explored the automation of complex eda flows by interfacing with eda tools via llms @xcite . specifically, chateda @xcite uses llms as "brains" of the agent to generate eda scripts, automating eda tool utilization, reducing the workload of circuit design engineers, and minimizing errors. although llms have shown potential in eda flow automation, significant challenges remain. firstly, although llms excel at understanding natural language, they lack specialized knowledge of eda tool usage. these tools are designed for specific tasks such as logic synthesis, floorplanning, placement, and routing, each requiring detailed domain-specific knowledge and familiarity with various eda tool interfaces. to solve these problems, llms can be fine-tuned on datasets containing tutorials and eda scripts specific to eda tools @xcite . however, this approach presents its problems. each eda platform has a unique set of commands and flows that must be mastered for effective use. if llms only focus on a specific eda tool or platform during the instruction tuning process will limit their cross-platform utility, potentially reducing the effectiveness in practical scenarios. furthermore, eda flows typically involve a sequence of intermediate steps. a single error in any of these steps can lead to failure in the overall process. this risk is compounded by the probabilistic nature of llms, which may pro-duce varying solutions to the same task. moreover, errors may occur in intermediate steps during a long-chain tool-calling process, introducing instability into the eda flow automation. to address these challenges, we introduce a multi-agent collaboration system, edaid, designed for eda flow automation through script generation in response to natural language instructions. this system is characterized by the collaboration of multiple agents, which are powered by the llms. as mentioned earlier, eda flow automation is challenging even for the greatest llm such as gpt-4 @xcite . consequently, we develop chipllama models, expert llms fine-tuned for eda flow automation. we specifically focus on improving the understanding of overall eda flow rather than simple eda tool usage during the finetuning process. we also employ few-shot chain-ofthought (cot) prompts for each agent to further develop the performance and portability through the in-context learning capability of llms. based on multiple agents, we propose the multi-agent system, edaid, to ensure stability and avoid erroneous intermediate steps during the long-chain eda tool calling process. in this system, multiple agents collaborate with divergent thoughts following different few-shot contexts and then make the final decision based on divergent thoughts, working in concert to automate the eda process. our edaid can interpret human instructions, plan eda tasks, and interface with eda tools through apis, serving as a valuable assistant in automating eda flows and eliminating the need for manual intervention. in summary, our contributions are as follows: ## chipllama-powered agent for an autonomous agent for eda flow automation, users can provide the eda task in natural language, and the agent will generate executable scripts to complete the eda task via an llm. to guarantee the performance and reliability of the overall flow, we introduce the expert llm, chipllama, as the "brain" of the single-agent system. furthermore, we apply few-shot cot prompts to the single-agent system to enhance the reasoning ability for eda flow automation as shown in figure 1 . ## multi-agent collaboration system automating eda flows within complex real-world scenarios poses a formidable challenge for singleagent systems (shown in figure 6 ). these com-plexities arise from the multidimensional nature of eda projects, which demand technical expertise and the need for a long-chain eda tool-calling process. as shown in figure 4 , the single-agent system will make mistakes in the intermediate steps during the complex eda task planning process, leading to failure in eda flow automation. in response, we introduce edaid, a collaborative multi-agent system designed for eda flow automation. unlike the single-agent system utilizing the vanilla cot, this system features multiple agents operating in synergy with self-consistency to resolve more complex eda tasks. in the following subsections, we will elucidate the mechanisms within the multi-agent system, which coordinate the efforts of multiple llm-powered agents to automate the eda flow. ## case studies in this section, we provide case studies about agents of edaid, including divergent thoughts agent (role r 0 ) and decision-making agent (role r 1 ), to demonstrate the core capabilities that enable edaid to function effectively. as shown in figure 6 , the divergent-thoughts agent can generate correct task planning pathways and the corresponding eda script that can automate the eda flow successfully. however, it is common that the divergent-thoughts agent makes mistakes in intermediate steps during the eda flow automation, which requires long-chain tool-calling capability. specifically, the "global_route" method does not have a parameter called "macro_place_channel". task planning pathway (wrong) eda script generation (wrong) eda task task planning pathway (correct) eda script generation (correct) eda task can you help me to experiment with different combinations of clock periods and channel values for the "router" design on the platform "asap7"? the task is to explore different combinations of clock periods and channel values for the "router" design on the "asap7" platform. the goal is to find the optimal combination that achieves the desired performance. to complete this task, follow these steps: step 1: initialize the "chateda" object. ## related works chain-of-thought. the cot paradigm @xcite encourages llms to break down complex problems into several intermediate steps, emulating the way humans reason through a problem. rather than directly outputting the final answer, llms are required to generate a step-by-step reasoning process, which can help models handle more complex tasks. self-consistency with cot (cot-sc) @xcite improves upon cot by generating different thought processes for the same problem and the output decision can be more reliable by explor-ing a richer set of thoughts. in this paper, we utilize the cot-sc paradigm to collaborate with various agents in edaid to perform eda task planning and script generation for eda flow automation. in-context learning. in-context learning @xcite has emerged as a transformative paradigm in machine learning, characterized by the meticulous training of models to perform specific tasks through examples and directives provided within an interactive, conversational framework. the ability of in-context learning emerges @xcite in large-scale, versatile llms @xcite @xcite . these models demonstrate an impressive ability to leverage their broad knowledge across various downstream tasks through incontext learning @xcite . in our research, we apply few-shot prompts to enhance the performance and reliability of each agent in edaid based on in-context learning. llm-powered agent system. single-agent systems driven by llms have demonstrated remarkable cognitive capabilities @xcite @xcite . these llm-powered agents can decompose complex tasks into manageable subtasks @xcite and methodically think through each component to make better decisions. moreover, the tool-eda task eda task can you help me check whether the provided script can complete the task and provide the reason. here is the task: i want to optimize performance at final stage for my design asjdk on nangate45. here is the script: def tune_performance(): @xmath0: {"minmax": [0, 100], "step": 5} } def optimize(tns_end_percent): @xmath1.setup(@xmath2, @xmath3.run_synthesis() eda.floorplan() eda.placement() eda.cts(@xmath4.get_metric(@xmath5, @xmath6, params) tune_performance() if the script can't complete the task, please provide a script that can complete the task. can you help me check whether the provided script can complete the task and provide the reason. here is the task: i want to perform cts for my design asjdk on nangate45. here is the script: def perform_cts(tns_end_percent): @xmath7.setup(@xmath8, @xmath9.run_synthesis() eda.floorplan() # run placement eda.placement(@xmath10.cts() perform_cts(10) if the script can't complete the task, please provide a script that can complete the task. ## conclusion automating eda flow by interfacing eda tools via apis is imperative to enhance efficiency in electronic design processes. in this study, we first introduce the chipllama-powered agent, collaborating with few-shot prompts for eda flow automation. specifically, chipllama models are expert llms instruction fine-tuned for the eda flow automation, which achieve the sota performance in eda script generation and demonstrate versatility across different platforms. meanwhile, different few-shot cot prompts can guide llms to generate different task planning pathways for divergent thoughts generation. building with divergent thoughts gen-eration and decision-making, we present edaid, a novel multi-agent collaboration system that utilizes multiple agents for eda tasks. this system adeptly handles intricate eda tasks assigned by designers, thereby automating the eda workflow effectively. our experiments demonstrate the effectiveness of our edaid. moreover, extensive experiments show the significant performance of our chipllama models compared to other llms in automating the eda flow. in conclusion, we anticipate that our work can catalyze the evolution of next-generation eda tools, inspiring advancements in the field.