Timing Analysis Agent: Autonomous Multi-Corner Multi-Mode (MCMM) Timing Debugging with Timing Debug Relation Graph

• URL: http://arxiv.org/abs/2504.11502v1
• Date: Tue, 15 Apr 2025 04:14:36 GMT
• Title: Timing Analysis Agent: Autonomous Multi-Corner Multi-Mode (MCMM) Timing Debugging with Timing Debug Relation Graph
• Authors: Jatin Nainani, Chia-Tung Ho, Anirudh Dhurka, Haoxing Ren,
• Abstract summary: Small metal pitches and increasing number of devices have led to longer turn-around-time for experienced human designers to debug timing issues.<n>Large Language Models (LLMs) have shown great promise across various tasks in language understanding and interactive decision-making.<n>We build a Timing Relation Graph (TDRG) that connects the reports with the relationships of debug traces from experienced timing engineers.
• Score: 1.6392250108065922
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Timing analysis is an essential and demanding verification method for Very Large Scale Integrated (VLSI) circuit design and optimization. In addition, it also serves as the cornerstone of the final sign-off, determining whether the chip is ready to be sent to the semiconductor foundry for fabrication. Recently, as the technology advance relentlessly, smaller metal pitches and the increasing number of devices have led to greater challenges and longer turn-around-time for experienced human designers to debug timing issues from the Multi-Corner Multi-Mode (MCMM) timing reports. As a result, an efficient and intelligent methodology is highly necessary and essential for debugging timing issues and reduce the turnaround times. Recently, Large Language Models (LLMs) have shown great promise across various tasks in language understanding and interactive decision-making, incorporating reasoning and actions. In this work, we propose a timing analysis agent, that is empowered by multi-LLMs task solving, and incorporates a novel hierarchical planning and solving flow to automate the analysis of timing reports from commercial tool. In addition, we build a Timing Debug Relation Graph (TDRG) that connects the reports with the relationships of debug traces from experienced timing engineers. The timing analysis agent employs the novel Agentic Retrieval Augmented Generation (RAG) approach, that includes agent and coding to retrieve data accurately, on the developed TDRG. In our studies, the proposed timing analysis agent achieves an average 98% pass-rate on a single-report benchmark and a 90% pass-rate for multi-report benchmark from industrial designs, demonstrating its effectiveness and adaptability.

Related papers

• Thinking Longer, Not Larger: Enhancing Software Engineering Agents via Scaling Test-Time Compute [61.00662702026523]
  We propose a unified Test-Time Compute scaling framework that leverages increased inference-time instead of larger models.<n>Our framework incorporates two complementary strategies: internal TTC and external TTC.<n>We demonstrate our textbf32B model achieves a 46% issue resolution rate, surpassing significantly larger models such as DeepSeek R1 671B and OpenAI o1.
  arXiv  Detail & Related papers  (2025-03-31T07:31:32Z)
• MAMM-Refine: A Recipe for Improving Faithfulness in Generation with Multi-Agent Collaboration [63.31211701741323]
  We extend multi-agent multi-model reasoning to generation, specifically to improving faithfulness through refinement.<n>We design intrinsic evaluations for each subtask, with our findings indicating that both multi-agent (multiple instances) and multi-model (diverse LLM types) approaches benefit error detection and critiquing.<n>We consolidate these insights into a final "recipe" called Multi-Agent Multi-Model Refinement (MAMM-Refine), where multi-agent and multi-model collaboration significantly boosts performance.
  arXiv  Detail & Related papers  (2025-03-19T14:46:53Z)
• A Time Series Multitask Framework Integrating a Large Language Model, Pre-Trained Time Series Model, and Knowledge Graph [1.3654846342364308]
  Time series analysis is crucial in fields like finance, transportation, and industry.<n>This paper proposes a novel time series multitask framework, called LTM, which integrates temporal features with textual descriptions.<n> Experiments on benchmark datasets show that LTM significantly outperforms existing methods.
  arXiv  Detail & Related papers  (2025-03-10T11:25:01Z)
• Multi2: Multi-Agent Test-Time Scalable Framework for Multi-Document Processing [35.686125031177234]
  Multi-Document Summarization (MDS) is a challenging task that focuses on extracting and synthesizing useful information from multiple lengthy documents.<n>We propose a novel framework that leverages inference-time scaling for this task.<n>We also introduce two new evaluation metrics: Consistency-Aware Preference (CAP) score and LLM Atom-Content-Unit (ACU) score.
  arXiv  Detail & Related papers  (2025-02-27T23:34:47Z)
• METAL: A Multi-Agent Framework for Chart Generation with Test-Time Scaling [100.33658998796064]
  We build a vision-language model (VLM) based multi-agent framework for effective automatic chart generation.<n>We propose METAL, a multi-agent framework that decomposes the task of chart generation into the iterative collaboration among specialized agents.
  arXiv  Detail & Related papers  (2025-02-24T21:01:39Z)
• Division-of-Thoughts: Harnessing Hybrid Language Model Synergy for Efficient On-Device Agents [5.566936703366701]
  Division-of-Thoughts (DoT) is a collaborative reasoning framework leveraging the synergy between local and cloud-based language models. DoT reduces the average reasoning time and API costs by 66.12% and 83.57%, while achieving comparable reasoning accuracy with the best baseline methods.
  arXiv  Detail & Related papers  (2025-02-06T02:40:25Z)
• Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Framework [81.29965270493238]
  We develop a specialized dataset aimed at enhancing the evaluation and fine-tuning of large language models (LLMs) for wireless communication applications.<n>The dataset includes a diverse set of multi-hop questions, including true/false and multiple-choice types, spanning varying difficulty levels from easy to hard.<n>We introduce a Pointwise V-Information (PVI) based fine-tuning method, providing a detailed theoretical analysis and justification for its use in quantifying the information content of training data.
  arXiv  Detail & Related papers  (2025-01-16T16:19:53Z)
• Multi-Agent Sampling: Scaling Inference Compute for Data Synthesis with Tree Search-Based Agentic Collaboration [81.45763823762682]
  This work aims to bridge the gap by investigating the problem of data synthesis through multi-agent sampling.<n>We introduce Tree Search-based Orchestrated Agents(TOA), where the workflow evolves iteratively during the sequential sampling process.<n>Our experiments on alignment, machine translation, and mathematical reasoning demonstrate that multi-agent sampling significantly outperforms single-agent sampling as inference compute scales.
  arXiv  Detail & Related papers  (2024-12-22T15:16:44Z)
• Towards Human-Level Understanding of Complex Process Engineering Schematics: A Pedagogical, Introspective Multi-Agent Framework for Open-Domain Question Answering [0.0]
  In the chemical and process industries, Process Flow Diagrams (PFDs) and Piping and Instrumentation Diagrams (P&IDs) are critical for design, construction, and maintenance. Recent advancements in Generative AI have shown promise in understanding and interpreting process diagrams for Visual Question Answering (VQA) We propose a secure, on-premises enterprise solution using a hierarchical, multi-agent Retrieval Augmented Generation (RAG) framework.
  arXiv  Detail & Related papers  (2024-08-24T19:34:04Z)
• Characterization of Large Language Model Development in the Datacenter [55.9909258342639]
  Large Language Models (LLMs) have presented impressive performance across several transformative tasks. However, it is non-trivial to efficiently utilize large-scale cluster resources to develop LLMs. We present an in-depth characterization study of a six-month LLM development workload trace collected from our GPU datacenter Acme.
  arXiv  Detail & Related papers  (2024-03-12T13:31:14Z)
• Multivariate Time Series characterization and forecasting of VoIP traffic in real mobile networks [9.637582917616703]
  Predicting the behavior of real-time traffic (e.g., VoIP) in mobility scenarios could help the operators to better plan their network infrastructures. This work proposes a forecasting analysis of crucial/QoE descriptors of VoIP traffic in a real mobile environment.
  arXiv  Detail & Related papers  (2023-07-13T09:21:39Z)
• ART: Automatic multi-step reasoning and tool-use for large language models [105.57550426609396]
  Large language models (LLMs) can perform complex reasoning in few- and zero-shot settings. Each reasoning step can rely on external tools to support computation beyond the core LLM capabilities. We introduce Automatic Reasoning and Tool-use (ART), a framework that uses frozen LLMs to automatically generate intermediate reasoning steps as a program.
  arXiv  Detail & Related papers  (2023-03-16T01:04:45Z)

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.