Intelligent4DSE: Optimizing High-Level Synthesis Design Space Exploration with Graph Neural Networks and Large Language Models

• URL: http://arxiv.org/abs/2504.19649v1
• Date: Mon, 28 Apr 2025 10:08:56 GMT
• Title: Intelligent4DSE: Optimizing High-Level Synthesis Design Space Exploration with Graph Neural Networks and Large Language Models
• Authors: Lei Xu, Shanshan Wang, Emmanuel Casseau, Chenglong Xiao,
• Abstract summary: We propose CoGNNs-LLMEA, a framework that integrates a graph neural network with task-adaptive message passing and a large language model-enhanced evolutionary algorithm.<n>As a predictive model, CoGNNs directly leverages intermediate representations generated from source code after compiler front-end processing, enabling prediction of quality of results (QoR) without invoking HLS tools.<n>CoGNNs achieves state-of-the-art prediction accuracy in post-HLS QoR prediction, reducing mean prediction errors by 2.8$times$ for latency and 3.4$times$ for resource utilization compared to baseline models
• Score: 3.8429489584622156
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High-level synthesis (HLS) design space exploration (DSE) is an optimization process in electronic design automation (EDA) that systematically explores high-level design configurations to achieve Pareto-optimal hardware implementations balancing performance, area, and power (PPA). To optimize this process, HLS prediction tasks often employ message-passing neural networks (MPNNs), leveraging complex architectures to achieve high accuracy. These predictors serve as evaluators in the DSE process, effectively bypassing the time-consuming estimations traditionally required by HLS tools. However, existing models often prioritize structural complexity and minimization of training loss, overlooking task-specific characteristics. Additionally, while evolutionary algorithms are widely used in DSE, they typically require extensive domain-specific knowledge to design effective crossover and mutation operators. To address these limitations, we propose CoGNNs-LLMEA, a framework that integrates a graph neural network with task-adaptive message passing and a large language model-enhanced evolutionary algorithm. As a predictive model, CoGNNs directly leverages intermediate representations generated from source code after compiler front-end processing, enabling prediction of quality of results (QoR) without invoking HLS tools. Due to its strong adaptability to tasks, CoGNNs can be tuned to predict post-HLS and post-implementation outcomes, effectively bridging the gap between high-level abstractions and physical implementation characteristics. CoGNNs achieves state-of-the-art prediction accuracy in post-HLS QoR prediction, reducing mean prediction errors by 2.8$\times$ for latency and 3.4$\times$ for resource utilization compared to baseline models.

Related papers

• Application-oriented automatic hyperparameter optimization for spiking neural network prototyping [0.0]
  This document uses the Neural Network Intelligence (NNI) toolkit as reference framework to present one such solution.<n>A summary of published works employing the presented pipeline is reported as possible source of insights into application-oriented HPO experiments for SNN prototyping.
  arXiv  Detail & Related papers  (2025-02-13T14:49:44Z)
• Task-Oriented Real-time Visual Inference for IoVT Systems: A Co-design Framework of Neural Networks and Edge Deployment [61.20689382879937]
  Task-oriented edge computing addresses this by shifting data analysis to the edge. Existing methods struggle to balance high model performance with low resource consumption. We propose a novel co-design framework to optimize neural network architecture.
  arXiv  Detail & Related papers  (2024-10-29T19:02:54Z)
• Set-based Neural Network Encoding Without Weight Tying [91.37161634310819]
  We propose a neural network weight encoding method for network property prediction.<n>Our approach is capable of encoding neural networks in a model zoo of mixed architecture.<n>We introduce two new tasks for neural network property prediction: cross-dataset and cross-architecture.
  arXiv  Detail & Related papers  (2023-05-26T04:34:28Z)
• End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes [52.818579746354665]
  This paper proposes the first end-to-end differentiable meta-BO framework that generalises neural processes to learn acquisition functions via transformer architectures. We enable this end-to-end framework with reinforcement learning (RL) to tackle the lack of labelled acquisition data.
  arXiv  Detail & Related papers  (2023-05-25T10:58:46Z)
• Intelligence Processing Units Accelerate Neuromorphic Learning [52.952192990802345]
  Spiking neural networks (SNNs) have achieved orders of magnitude improvement in terms of energy consumption and latency. We present an IPU-optimized release of our custom SNN Python package, snnTorch.
  arXiv  Detail & Related papers  (2022-11-19T15:44:08Z)
• Hybrid Graph Models for Logic Optimization via Spatio-Temporal Information [15.850413267830522]
  Two major concerns that may impede production-ready ML applications in EDA are accuracy requirements and generalization capability. We propose hybrid graph neural network (GNN) based approaches towards highly accurate quality-of-result (QoR) estimations. Evaluation on 3.3 million data points shows that the testing mean absolute percentage error (MAPE) on designs seen unseen during training are no more than 1.2% and 3.1%.
  arXiv  Detail & Related papers  (2022-01-20T21:12:22Z)
• High-Level Synthesis Performance Prediction using GNNs: Benchmarking, Modeling, and Advancing [21.8349113634555]
  Agile hardware development requires fast and accurate circuit quality evaluation from early design stages. We propose a rapid and accurate performance modeling, exploiting the representation power of graph neural networks (GNNs) by representing C/C++ programs as graphs. Our proposed predictor largely outperforms HLS by up to 40X and excels existing predictors by 2X to 5X in terms of resource usage and timing prediction.
  arXiv  Detail & Related papers  (2022-01-18T09:53:48Z)
• Hybrid SNN-ANN: Energy-Efficient Classification and Object Detection for Event-Based Vision [64.71260357476602]
  Event-based vision sensors encode local pixel-wise brightness changes in streams of events rather than image frames. Recent progress in object recognition from event-based sensors has come from conversions of deep neural networks. We propose a hybrid architecture for end-to-end training of deep neural networks for event-based pattern recognition and object detection.
  arXiv  Detail & Related papers  (2021-12-06T23:45:58Z)
• A Graph Deep Learning Framework for High-Level Synthesis Design Space Exploration [11.154086943903696]
  High-Level Synthesis is a solution for fast prototyping application-specific hardware. We propose HLS, for the first time in the literature, graph neural networks that jointly predict acceleration performance and hardware costs. We show that our approach achieves prediction accuracy comparable with that of commonly used simulators.
  arXiv  Detail & Related papers  (2021-11-29T18:17:45Z)
• Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach [81.5261621619557]
  We propose an adaptive anomaly detection scheme with hierarchical edge computing (HEC) We first construct multiple anomaly detection DNN models with increasing complexity, and associate each of them to a corresponding HEC layer. Then, we design an adaptive model selection scheme that is formulated as a contextual-bandit problem and solved by using a reinforcement learning policy network.
  arXiv  Detail & Related papers  (2021-08-09T08:45:47Z)
• A Meta-Learning Approach to the Optimal Power Flow Problem Under Topology Reconfigurations [69.73803123972297]
  We propose a DNN-based OPF predictor that is trained using a meta-learning (MTL) approach. The developed OPF-predictor is validated through simulations using benchmark IEEE bus systems.
  arXiv  Detail & Related papers  (2020-12-21T17:39:51Z)
• Error-feedback stochastic modeling strategy for time series forecasting with convolutional neural networks [11.162185201961174]
  We propose a novel Error-feedback Modeling (ESM) strategy to construct a random Convolutional Network (ESM-CNN) Neural time series forecasting task. The proposed ESM-CNN not only outperforms the state-of-art random neural networks, but also exhibits stronger predictive power and less computing overhead in comparison to trained state-of-art deep neural network models.
  arXiv  Detail & Related papers  (2020-02-03T13:30:29Z)

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.