AutoFlows++: Hierarchical Message Flow Mining for System on Chip Designs
- URL: http://arxiv.org/abs/2604.15359v1
- Date: Sun, 12 Apr 2026 15:44:43 GMT
- Title: AutoFlows++: Hierarchical Message Flow Mining for System on Chip Designs
- Abstract summary: AutoFlows++ is a design-architecture-guided hierarchical framework for mining message flows from communication traces of complex designs.<n>Results show that AutoFlows++ significantly improves flow extraction accuracy compared with prior approaches.
- Score: 6.0174402828965725
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Understanding communication behavior in modern system-on-chip (SoC) designs is critical for functional verification, performance analysis, and post-silicon debugging. Communication traces capture message exchanges among system components and provide valuable insights into system behavior. However, deriving concise communication specifications from such traces remains challenging due to interleaved instances of communication flows, and ambiguous causal relationships among messages. Existing mining approaches often struggle with scalability and ambiguity when traces contain complex interleaving of message patterns across multiple components. These conditions often lead to an explosion in the number of candidate flows and inaccurate extraction of communication behaviors. This paper presents AutoFlows++, a design-architecture-guided hierarchical framework for mining message flows from communication traces of complex SoC designs. AutoFlows++ operates in two stages: local mining followed by global mining. In the local mining stage, simple communication patterns are extracted from traces observed at individual communication interfaces between components. In the global mining stage, these local patterns are composed to identify higher-level message flows that characterize communication behavior across multiple components. Experimental results on both synthetic traces and traces generated from SoC models in GEM5 demonstrate that AutoFlows++ significantly improves flow extraction accuracy compared with prior approaches, highlighting its effectiveness for practical SoC validation tasks.
Related papers
- From Semantic to Token Communication: The Next Paradigm for Large-Model-Driven 6G Intelligent Connectivity [94.51565447227847]
6G networks are driving communication systems from reliable bit delivery toward meaning-aware and task-oriented connectivity.<n>Large models (LMs) with strong multimodal understanding and generation capabilities have accelerated this shift.<n>Current LM-driven semantic communication (SemCom) remains fragmented.<n>We argue that tokens provide a natural candidate for this missing abstraction.
arXiv Detail & Related papers (2026-09-09T18:11:38Z) - ContextFlow: In-Context Flow Matching for Robot Manipulation [91.60992853468242]
We introduce ContextFlow, a conditional flow-matching model that learns continuous action distributions for in-context imitation learning.<n>On LIBERO, ContextFlow outperforms ICRT by 35 percentage points in average success rate on unseen task configurations.<n>On real robots, it generalizes to unseen configurations of both single-arm and bimanual tasks, achieving 40% success on a new pen-uncapping configuration.
arXiv Detail & Related papers (2026-09-06T21:53:58Z) - Inference-Time Graph Engineering for Multi-Agent LLM Workflows [51.56038180639833]
We introduce ReActNet, a training-free framework that compiles a query and a set of role-specialized agents into a sequence of directed communication graphs.<n>We show that ReActNet consistently improves over fixed-topology and learned-topology baselines while maintaining competitive inference cost.
arXiv Detail & Related papers (2026-09-04T23:32:11Z) - Microflow: Microarchitectural Causal Observability for Deep Cross-Layer Analysis and Optimization [6.343991956675757]
This paper introduces Microflow, an observability framework elevating causality to a first-class analytical object.<n>Microflow transforms execution traces into the Microflow Intermediate Representation (MFIR), explicitly capturing dependencies across software semantics, instructions, microarchitectural events, and hardware resources.
arXiv Detail & Related papers (2026-07-14T18:36:19Z) - Contextualize-then-Aggregate: Circuits for In-Context Learning in Gemma-2 2B [51.74607395697567]
In-Context Learning (ICL) is an intriguing ability of large language models (LLMs)<n>We use causal interventions to identify information flow in Gemma-2 2B for five naturalistic ICL tasks.<n>We find that the model infers task information using a two-step strategy we call contextualize-then-aggregate.
arXiv Detail & Related papers (2025-03-31T18:33:55Z) - MIETT: Multi-Instance Encrypted Traffic Transformer for Encrypted Traffic Classification [59.96233305733875]
Classifying traffic is essential for detecting security threats and optimizing network management.<n>We propose a Multi-Instance Encrypted Traffic Transformer (MIETT) to capture both token-level and packet-level relationships.<n>MIETT achieves results across five datasets, demonstrating its effectiveness in classifying encrypted traffic and understanding complex network behaviors.
arXiv Detail & Related papers (2024-12-19T12:52:53Z) - Large Language Models as Realistic Microservice Trace Generators [48.730974361862366]
This paper proposes a first-of-a-kind approach that relies on training a large language model (LLM) to generate synthetic workload traces.<n>We show that TraceLLM produces diverse, realistic traces under varied conditions, outperforming existing approaches in both accuracy and validity.<n>TraceLLM adapts to downstream trace-related tasks, such as predicting key trace features and infilling missing data.
arXiv Detail & Related papers (2024-12-16T12:48:04Z) - Online Joint Fine-tuning of Multi-Agent Flows [12.851745991007169]
I describe a procedure for online joint fine-tuning of an entire flow inspired by the Learning to Search framework.
The approach leverages simulator access to reduce preferences over entire episodes to preferences over individual node outputs.
I apply to the multi-hop QA dataset Musique achieving a state-of-the-art result.
arXiv Detail & Related papers (2024-06-06T21:21:03Z) - Inferring Message Flows From System Communication Traces [5.159745269633967]
This paper proposes a method for automatically inferring message flow specifications from the communication traces of a system-on-chip (SoC) design.
The proposed method relieves the burden of manual development and maintenance of such specifications on human designers.
The effectiveness of the proposed method is evaluated on both synthetic traces and traces generated from executing several system models in GEM5.
arXiv Detail & Related papers (2024-05-21T00:34:35Z) - Semantic-Native Communication: A Simplicial Complex Perspective [50.099494681671224]
We study semantic communication from a topological space perspective.
A transmitter first maps its data into a $k$-order simplicial complex and then learns its high-order correlations.
The receiver decodes the structure and infers the missing or distorted data.
arXiv Detail & Related papers (2022-10-30T22:33:44Z) - Boosting Entity Mention Detection for Targetted Twitter Streams with
Global Contextual Embeddings [7.337247167823921]
Two important tasks related to the automatic extraction and analysis of information in Microblogs are Entity Mention Detection (EMD) and Entity Detection (ED)
The state-of-the-art systems aim to model the non-literary nature of microblog text by training upon offline static datasets.
But given the constantly evolving nature of microblog streams, detecting all entity mentions from such varying yet limited context remains a difficult problem.
We propose a framework named Globalizer, better suited for the execution of learners on microblog streams.
arXiv Detail & Related papers (2022-01-28T01:44:05Z) - Mining Message Flows using Recurrent Neural Networks for System-on-Chip
Designs [6.050748152029478]
We develop a specification mining approach that automatically extracts sequential patterns from transaction-level traces.
The proposed approach is capable of extracting various patterns on system-level specifications from the highly concurrent execution traces.
arXiv Detail & Related papers (2020-04-29T17:52:53Z)
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.