LLM-DSE: Searching Accelerator Parameters with LLM Agents

• URL: http://arxiv.org/abs/2505.12188v2
• Date: Tue, 20 May 2025 08:29:37 GMT
• Title: LLM-DSE: Searching Accelerator Parameters with LLM Agents
• Authors: Hanyu Wang, Xinrui Wu, Zijian Ding, Su Zheng, Chengyue Wang, Tony Nowatzki, Yizhou Sun, Jason Cong,
• Abstract summary: LLM-DSE is a multi-agent framework designed specifically for optimizing HLS directives.<n>Our explorer coordinates four agents: Router, Specialists, Arbitrator, and Critic.<n>LLM-DSE achieves substantial $2.55times$ performance gains over state-of-the-art methods.
• Score: 34.75581582648836
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Even though high-level synthesis (HLS) tools mitigate the challenges of programming domain-specific accelerators (DSAs) by raising the abstraction level, optimizing hardware directive parameters remains a significant hurdle. Existing heuristic and learning-based methods struggle with adaptability and sample efficiency. We present LLM-DSE, a multi-agent framework designed specifically for optimizing HLS directives. Combining LLM with design space exploration (DSE), our explorer coordinates four agents: Router, Specialists, Arbitrator, and Critic. These multi-agent components interact with various tools to accelerate the optimization process. LLM-DSE leverages essential domain knowledge to identify efficient parameter combinations while maintaining adaptability through verbal learning from online interactions. Evaluations on the HLSyn dataset demonstrate that LLM-DSE achieves substantial $2.55\times$ performance gains over state-of-the-art methods, uncovering novel designs while reducing runtime. Ablation studies validate the effectiveness and necessity of the proposed agent interactions. Our code is open-sourced here: https://github.com/Nozidoali/LLM-DSE.

Related papers

• Agentic Reinforced Policy Optimization [66.96989268893932]
  Large-scale reinforcement learning with verifiable rewards (RLVR) has demonstrated its effectiveness in harnessing the potential of large language models (LLMs) for single-turn reasoning tasks.<n>Current RL algorithms inadequately balance the models' intrinsic long-horizon reasoning capabilities and their proficiency in multi-turn tool interactions.<n>We propose Agentic Reinforced Policy Optimization (ARPO), a novel agentic RL algorithm tailored for training multi-turn LLM-based agents.
  arXiv  Detail & Related papers  (2025-07-26T07:53:11Z)
• Graft: Integrating the Domain Knowledge via Efficient Parameter Synergy for MLLMs [56.76586846269894]
  Multimodal Large Language Models (MLLMs) have achieved success across various domains.<n>Despite its importance, the study of knowledge sharing among domain-specific MLLMs remains largely underexplored.<n>We propose a unified parameter integration framework that enables modular composition of expert capabilities.
  arXiv  Detail & Related papers  (2025-06-30T15:07:41Z)
• ORFS-agent: Tool-Using Agents for Chip Design Optimization [0.8088986164437757]
  Large Language Models (LLMs) offer new opportunities for learning and reasoning within such high-dimensional optimization tasks.<n>We introduce ORFS-agent, an LLM-based iterative optimization agent that automates parameter tuning in an open-source hardware design flow.<n>Our empirical evaluations on two different technology nodes and a range of circuit benchmarks indicate that ORFS-agent can improve both routed wirelength and effective clock period by over 13%.
  arXiv  Detail & Related papers  (2025-06-10T01:38:57Z)
• TUMS: Enhancing Tool-use Abilities of LLMs with Multi-structure Handlers [8.34574238496256]
  We propose TUMS, a novel framework designed to enhance the tool-use capabilities of large language models.<n>Our framework consists of four key components: (1) an intent recognizer that identifies the user's intent to help LLMs better understand the task; (2) a task decomposer that breaks down complex tasks into simpler subtasks, each involving a tool call; and (3) a subtask processor equipped with multi-structure handlers to generate accurate parameters.<n>Our empirical studies have evidenced the effectiveness and efficiency of the TUMS framework with an average of 19.6% and 50.6% improvement separately on easy and hard
  arXiv  Detail & Related papers  (2025-05-13T09:57:28Z)
• Optuna vs Code Llama: Are LLMs a New Paradigm for Hyperparameter Tuning? [42.362388367152256]
  Large language models (LLMs) are used to fine-tune a parameter-efficient version of Code Llama using LoRA.<n>Our method achieves competitive or superior results in terms of Root Mean Square Error (RMSE) while significantly reducing computational overhead.
  arXiv  Detail & Related papers  (2025-04-08T13:15:47Z)
• A Survey on the Optimization of Large Language Model-based Agents [16.733092886211097]
  Large Language Models (LLMs) have been widely adopted in various fields, becoming essential for autonomous decision-making and interactive tasks.<n>However, current work typically relies on prompt design or fine-tuning strategies applied to vanilla LLMs.<n>We provide a comprehensive review of LLM-based agent optimization approaches, categorizing them into parameter-driven and parameter-free methods.
  arXiv  Detail & Related papers  (2025-03-16T10:09:10Z)
• Scaling Autonomous Agents via Automatic Reward Modeling And Planning [52.39395405893965]
  Large language models (LLMs) have demonstrated remarkable capabilities across a range of tasks.<n>However, they still struggle with problems requiring multi-step decision-making and environmental feedback.<n>We propose a framework that can automatically learn a reward model from the environment without human annotations.
  arXiv  Detail & Related papers  (2025-02-17T18:49:25Z)
• Training of Scaffolded Language Models with Language Supervision: A Survey [62.59629932720519]
  This survey organizes the literature on the design and optimization of emerging structures around post-trained LMs.<n>We refer to this overarching structure as scaffolded LMs and focus on LMs that are integrated into multi-step processes with tools.
  arXiv  Detail & Related papers  (2024-10-21T18:06:25Z)
• Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language Models [79.41139393080736]
  Large language models (LLMs) have rapidly advanced and demonstrated impressive capabilities. In-Context Learning (ICL) and. Efficient Fine-Tuning (PEFT) are currently two mainstream methods for augmenting. LLMs to downstream tasks. We propose Reference Trustable Decoding (RTD), a paradigm that allows models to quickly adapt to new tasks without fine-tuning.
  arXiv  Detail & Related papers  (2024-09-30T10:48:20Z)
• Large Language Model as a Catalyst: A Paradigm Shift in Base Station Siting Optimization [62.16747639440893]
  Large language models (LLMs) and their associated technologies advance, particularly in the realms of prompt engineering and agent engineering.<n>Our proposed framework incorporates retrieval-augmented generation (RAG) to enhance the system's ability to acquire domain-specific knowledge and generate solutions.
  arXiv  Detail & Related papers  (2024-08-07T08:43:32Z)
• New Solutions on LLM Acceleration, Optimization, and Application [14.995654657013741]
  Large Language Models (LLMs) have become extremely potent instruments with exceptional capacities for comprehending and producing human-like text in a range of applications. However, the increasing size and complexity of LLMs present significant challenges in both training and deployment. We provide a review of recent advancements and research directions aimed at addressing these challenges.
  arXiv  Detail & Related papers  (2024-06-16T11:56:50Z)
• Towards Efficient LLM Grounding for Embodied Multi-Agent Collaboration [70.09561665520043]
  We propose a novel framework for multi-agent collaboration that introduces Reinforced Advantage feedback (ReAd) for efficient self-refinement of plans. We provide theoretical analysis by extending advantage-weighted regression in reinforcement learning to multi-agent systems. Experiments on Over-AI and a difficult variant of RoCoBench show that ReAd surpasses baselines in success rate, and also significantly decreases the interaction steps of agents.
  arXiv  Detail & Related papers  (2024-05-23T08:33:19Z)
• Large Language Model Agent for Hyper-Parameter Optimization [27.191259249128137]
  We introduce a novel paradigm leveraging Large Language Models (LLMs) to automate hyperparameter optimization across diverse machine learning tasks.<n>AgentHPO processes the task information autonomously, conducts experiments with specific hyper parameters, and iteratively optimize them.<n>This human-like optimization process largely reduces the number of required trials, simplifies the setup process, and enhances interpretability and user trust.
  arXiv  Detail & Related papers  (2024-02-02T20:12:05Z)
• FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning [70.38817963253034]
  This paper first discusses these challenges of federated fine-tuning LLMs, and introduces our package FS-LLM as a main contribution. We provide comprehensive federated parameter-efficient fine-tuning algorithm implementations and versatile programming interfaces for future extension in FL scenarios. We conduct extensive experiments to validate the effectiveness of FS-LLM and benchmark advanced LLMs with state-of-the-art parameter-efficient fine-tuning algorithms in FL settings.
  arXiv  Detail & Related papers  (2023-09-01T09:40:36Z)
• Enabling Intelligent Interactions between an Agent and an LLM: A Reinforcement Learning Approach [31.6589518077397]
  Large language models (LLMs) encode a vast amount of world knowledge acquired from massive text datasets. LLMs can assist an embodied agent in solving complex sequential decision making tasks by providing high-level instructions. We propose When2Ask, a reinforcement learning based approach that learns when it is necessary to query LLMs for high-level instructions.
  arXiv  Detail & Related papers  (2023-06-06T11:49:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.