Einblick - Machine Learning - # Synthetic Data Generation for High-Level Synthesis Design Space Exploration

Generating High-Fidelity Synthetic Data for System-Level High-Level Synthesis Design Space Exploration using Generative Machine Learning

Q: How can the Vaegan approach be extended to generate synthetic data for other hardware design domains beyond high-level synthesis, such as register-transfer level (RTL) design or circuit-level design

The Vaegan approach can be extended to generate synthetic data for other hardware design domains beyond high-level synthesis by adapting the input transformation and generative models to suit the specific requirements of those domains. For RTL design, where the focus is on the low-level hardware description, the input transformation stage would involve converting RTL-specific data such as signal assignments, clocking schemes, and module hierarchies into a format readable by the network. The generative models, whether VAE or GAN, would need to be tailored to capture the intricacies of RTL design, such as timing constraints, signal propagation, and resource utilization. By training the models on a dataset of RTL designs and their corresponding characteristics, Vaegan could learn to generate synthetic RTL data that mirrors the real-world distribution. Similarly, for circuit-level design, the input transformation would involve encoding circuit components, interconnections, and performance metrics into a suitable input format for the network. The generative models would need to understand the complex interactions between circuit elements, noise considerations, and power consumption patterns to generate realistic circuit-level data. By fine-tuning the models on a dataset of circuit designs and their performance parameters, Vaegan could produce synthetic data that accurately represents circuit-level characteristics. In essence, the key to extending Vaegan to other hardware design domains lies in customizing the input transformation process and the generative models to capture the unique features and requirements of each domain. By training the models on domain-specific datasets, Vaegan can effectively generate high-fidelity synthetic data for a wide range of hardware design applications.

Q: What are the potential limitations of using generative machine learning models like VAE and GAN for synthetic data generation, and how can these limitations be addressed

Generative machine learning models like VAE and GAN have certain limitations when used for synthetic data generation, which can impact the quality and fidelity of the generated data. Some potential limitations include: Mode Collapse: GANs, in particular, are prone to mode collapse, where the generator produces limited varieties of outputs, leading to a lack of diversity in the synthetic data. This can result in the generated data not fully representing the underlying distribution of the real data. Training Instability: Both VAE and GAN models can suffer from training instability, where the models struggle to converge or exhibit erratic behavior during training. This instability can affect the quality of the generated data and make it challenging to achieve high fidelity. Overfitting: Generative models may overfit to the training data, capturing noise or specific patterns that are not representative of the underlying data distribution. This can result in synthetic data that does not generalize well to unseen data. To address these limitations, several strategies can be employed: Regularization: Introducing regularization techniques such as dropout, weight decay, or early stopping can help prevent overfitting and improve the generalization of the models. Architectural Modifications: Adjusting the architecture of the models, such as adding more layers, changing activation functions, or modifying the loss functions, can help stabilize training and mitigate issues like mode collapse. Data Augmentation: Increasing the diversity of the training data through data augmentation techniques can help the models learn a more robust representation of the data distribution and reduce the risk of overfitting. By carefully addressing these limitations through appropriate model design, training strategies, and data preprocessing techniques, the quality and reliability of synthetic data generated by VAE and GAN models can be significantly improved.

Q: What other applications or research areas could benefit from the high-fidelity synthetic data generation capabilities demonstrated by the Vaegan approach

The high-fidelity synthetic data generation capabilities demonstrated by the Vaegan approach have broad applications across various domains beyond high-level synthesis. Some potential applications and research areas that could benefit from this capability include: Medical Imaging: Synthetic data generation can be used to create realistic medical imaging datasets for training machine learning models in areas such as MRI reconstruction, CT image denoising, and ultrasound image enhancement. High-fidelity synthetic data can help improve the performance and generalization of medical imaging algorithms. Autonomous Vehicles: Generating synthetic sensor data for autonomous vehicles can aid in training perception systems for object detection, lane tracking, and decision-making. Realistic synthetic data can enhance the robustness and reliability of autonomous driving algorithms. Natural Language Processing: Synthetic data generation can be applied to create diverse text datasets for training language models, sentiment analysis, and text generation tasks. High-fidelity synthetic data can improve the accuracy and adaptability of natural language processing models. Robotics: Generating synthetic sensor data for robotic systems can facilitate training robotic control algorithms for manipulation, navigation, and object recognition. Realistic synthetic data can enhance the performance and efficiency of robotic systems in various applications. By leveraging the capabilities of high-fidelity synthetic data generation demonstrated by Vaegan, these and other research areas can benefit from improved data quality, increased dataset diversity, and enhanced model performance.

Kernkonzepte

A novel approach called Vaegan that employs generative machine learning to generate synthetic data that closely mirrors the distribution of real high-level synthesis data, enabling more comprehensive system-level design space exploration.

Zusammenfassung

The paper proposes a novel approach called Vaegan to generate synthetic data for system-level high-level synthesis (HLS) design space exploration (DSE). Existing HLS benchmarks and datasets are limited in their diversity and may not cover complex, multi-component system-level scenarios.

Vaegan consists of three stages:

Input Transformation: Diverse HLS data (directives, report estimation, post-synthesis, post-implementation) is transformed into a binary fixed-point format to enable handling both continuous and discrete data types.
Generative Models: Vaegan explores two generative machine learning models - Variational Autoencoder (VAE) and Generative Adversarial Network (GAN) - to generate synthetic HLS data. The authors find that the VAE-based MLPVAE model outperforms the GAN-based DCGAN model in terms of generating high-fidelity synthetic data.
Model Evaluation: The generated synthetic data is evaluated using state-of-the-art metrics like Maximum Mean Discrepancy (MMD), Sum of the Square of the Distances (SSD), Percentage Root-mean-square Difference (PRD), and Cosine Similarity (COSS) to ensure it closely matches the distribution of real HLS data.

Experimental results show that Vaegan, particularly the MLPVAE model, generates synthetic HLS data that is 44.05% closer to the real data distribution compared to prior works. The authors also demonstrate the practical usefulness of Vaegan through a case study on a wearable pregnancy monitoring system, where the synthetic data generated by Vaegan expands the Pareto frontier and uncovers new Pareto-optimal solutions not present in the original dataset.

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Statistiken

The HLS data used in the experiments was obtained from the HLSDataset, which includes HLS directives, HLS report estimation, post-synthesis, and partial post-implementation data.
The dataset contains 9,557 samples without HLS directives and 3,717 samples with HLS directives for the FPGA part xc7v585tffg1157-3.
The input data was transformed into a 32-bit binary fixed-point format, with 20 bits for the integer portion and 12 bits for the fractional portion.

Zitate

"Generating new data using existing HLS benchmarks can be cumbersome, given the expertise and time required to effectively generate data for different HLS designs and directives."
"Synthetic data offers numerous benefits for both ML-based and heuristic-based HLS DSE. For instance, it is cost effective and time efficient, and provides data diversity."

Wichtige Erkenntnisse aus

Skip the Benchmark: Generating System-Level High-Level Synthesis Data using Generative Machine Learning

by Yuchao Liao,... um arxiv.org 04-24-2024

https://arxiv.org/pdf/2404.14754.pdf

Skip the Benchmark: Generating System-Level High-Level Synthesis Data using Generative Machine Learning

Tiefere Fragen

How can the Vaegan approach be extended to generate synthetic data for other hardware design domains beyond high-level synthesis, such as register-transfer level (RTL) design or circuit-level design

The Vaegan approach can be extended to generate synthetic data for other hardware design domains beyond high-level synthesis by adapting the input transformation and generative models to suit the specific requirements of those domains. For RTL design, where the focus is on the low-level hardware description, the input transformation stage would involve converting RTL-specific data such as signal assignments, clocking schemes, and module hierarchies into a format readable by the network. The generative models, whether VAE or GAN, would need to be tailored to capture the intricacies of RTL design, such as timing constraints, signal propagation, and resource utilization. By training the models on a dataset of RTL designs and their corresponding characteristics, Vaegan could learn to generate synthetic RTL data that mirrors the real-world distribution.
Similarly, for circuit-level design, the input transformation would involve encoding circuit components, interconnections, and performance metrics into a suitable input format for the network. The generative models would need to understand the complex interactions between circuit elements, noise considerations, and power consumption patterns to generate realistic circuit-level data. By fine-tuning the models on a dataset of circuit designs and their performance parameters, Vaegan could produce synthetic data that accurately represents circuit-level characteristics.
In essence, the key to extending Vaegan to other hardware design domains lies in customizing the input transformation process and the generative models to capture the unique features and requirements of each domain. By training the models on domain-specific datasets, Vaegan can effectively generate high-fidelity synthetic data for a wide range of hardware design applications.

What are the potential limitations of using generative machine learning models like VAE and GAN for synthetic data generation, and how can these limitations be addressed

Generative machine learning models like VAE and GAN have certain limitations when used for synthetic data generation, which can impact the quality and fidelity of the generated data. Some potential limitations include:

Mode Collapse: GANs, in particular, are prone to mode collapse, where the generator produces limited varieties of outputs, leading to a lack of diversity in the synthetic data. This can result in the generated data not fully representing the underlying distribution of the real data.

Training Instability: Both VAE and GAN models can suffer from training instability, where the models struggle to converge or exhibit erratic behavior during training. This instability can affect the quality of the generated data and make it challenging to achieve high fidelity.

Overfitting: Generative models may overfit to the training data, capturing noise or specific patterns that are not representative of the underlying data distribution. This can result in synthetic data that does not generalize well to unseen data.

To address these limitations, several strategies can be employed:

Regularization: Introducing regularization techniques such as dropout, weight decay, or early stopping can help prevent overfitting and improve the generalization of the models.
Architectural Modifications: Adjusting the architecture of the models, such as adding more layers, changing activation functions, or modifying the loss functions, can help stabilize training and mitigate issues like mode collapse.
Data Augmentation: Increasing the diversity of the training data through data augmentation techniques can help the models learn a more robust representation of the data distribution and reduce the risk of overfitting.
By carefully addressing these limitations through appropriate model design, training strategies, and data preprocessing techniques, the quality and reliability of synthetic data generated by VAE and GAN models can be significantly improved.

What other applications or research areas could benefit from the high-fidelity synthetic data generation capabilities demonstrated by the Vaegan approach

The high-fidelity synthetic data generation capabilities demonstrated by the Vaegan approach have broad applications across various domains beyond high-level synthesis. Some potential applications and research areas that could benefit from this capability include:

Medical Imaging: Synthetic data generation can be used to create realistic medical imaging datasets for training machine learning models in areas such as MRI reconstruction, CT image denoising, and ultrasound image enhancement. High-fidelity synthetic data can help improve the performance and generalization of medical imaging algorithms.

Autonomous Vehicles: Generating synthetic sensor data for autonomous vehicles can aid in training perception systems for object detection, lane tracking, and decision-making. Realistic synthetic data can enhance the robustness and reliability of autonomous driving algorithms.

Natural Language Processing: Synthetic data generation can be applied to create diverse text datasets for training language models, sentiment analysis, and text generation tasks. High-fidelity synthetic data can improve the accuracy and adaptability of natural language processing models.

Robotics: Generating synthetic sensor data for robotic systems can facilitate training robotic control algorithms for manipulation, navigation, and object recognition. Realistic synthetic data can enhance the performance and efficiency of robotic systems in various applications.

By leveraging the capabilities of high-fidelity synthetic data generation demonstrated by Vaegan, these and other research areas can benefit from improved data quality, increased dataset diversity, and enhanced model performance.