Dataflow-Driven GPU-Accelerated Global Placement Framework for Efficient Placement of Machine Learning Accelerators

Core Concepts

A new and fast GPU-accelerated global placement framework, DG-RePlAce, that exploits the inherent dataflow and datapath structures of machine learning accelerators to achieve superior placement results.

Abstract

The paper presents DG-RePlAce, a new GPU-accelerated global placement framework that leverages the dataflow and datapath structures of machine learning accelerators to achieve high-quality placement results.
Key highlights:

DG-RePlAce is built on top of the OpenROAD infrastructure, enabling easy adaptation and integration with other enhancements.
It incorporates efficient data structures and algorithms to further speed up the global placement process, achieving 22.49X and 1.75X faster runtime compared to RePlAce and DREAMPlace, respectively.
Experimental results on various machine learning accelerator designs show that DG-RePlAce achieves 10% and 7% reduction in routed wirelength, and 31% and 34% reduction in total negative slack, compared to RePlAce and DREAMPlace, respectively.
The dataflow-driven approach is not limited to machine learning accelerators, as demonstrated by the significant improvements on the TILOS MacroPlacement Benchmarks.
Future work includes incorporating density screens, ML-based multi-objective optimization, and improving the runtime of physical hierarchy extraction.

Stats

Compared to RePlAce, DG-RePlAce achieves an average reduction in routed wirelength by 10% and total negative slack by 31%.
Compared to DREAMPlace, DG-RePlAce achieves an average reduction in routed wirelength by 7% and total negative slack by 34%.

Quotes

"DG-RePlAce is built on top of the OpenROAD infrastructure with a permissive open-source license, enabling other researchers to readily adapt it for other enhancements."
"Experimental results on the two largest TILOS MacroPlacement Benchmarks testcases show that compared with RePlAce and DREAMPlace, DG-RePlAce achieves much better timing metrics (WNS and TNS) measured post-route optimization."

Key Insights Distilled From

DG-RePlAce: A Dataflow-Driven GPU-Accelerated Analytical Global Placement Framework for Machine Learning Accelerators

by Andrew B. Ka... at arxiv.org 04-23-2024

https://arxiv.org/pdf/2404.13049.pdf

Deeper Inquiries

How can the dataflow-driven methodology be extended to handle other types of accelerators beyond machine learning, such as domain-specific accelerators for signal processing or cryptography?

The dataflow-driven methodology can be extended to handle other types of accelerators by adapting the placement framework to the specific dataflow and datapath structures of those accelerators. For domain-specific accelerators for signal processing or cryptography, the following approaches can be considered:

Customized Constraints: Develop specialized constraints that capture the unique dataflow patterns and datapath regularities of signal processing or cryptography accelerators. This may involve defining virtual connections, pseudo nets, or other constraints tailored to the specific requirements of these accelerators.

Algorithm Optimization: Modify the placement algorithms to optimize for the specific characteristics of signal processing or cryptography accelerators. This may include fine-tuning the wirelength gradient computation, density screens, or other optimization techniques to align with the dataflow patterns of these accelerators.

Integration of Domain-Specific Knowledge: Incorporate domain-specific knowledge into the placement process. This could involve leveraging insights from signal processing or cryptography experts to guide the placement of components based on their functional relationships and data dependencies.

Validation and Testing: Thoroughly validate the extended methodology on a diverse set of benchmarks and real-world designs from the signal processing and cryptography domains to ensure its effectiveness and reliability across different types of accelerators.

By customizing the dataflow-driven methodology to suit the requirements of domain-specific accelerators, it is possible to achieve optimized placement solutions that enhance performance, power efficiency, and overall quality of results for these specialized applications.

How can the potential challenges in incorporating ML-based multi-objective optimization techniques into the DG-RePlAce framework to achieve better tradeoffs across wirelength, congestion, power and timing be addressed?

Incorporating ML-based multi-objective optimization techniques into the DG-RePlAce framework can offer significant benefits in achieving better tradeoffs across wirelength, congestion, power, and timing. However, there are several challenges that need to be addressed:

Data Collection and Feature Engineering: Collecting relevant data and engineering informative features for the ML models to effectively capture the tradeoffs between different objectives is crucial. This may involve extracting design metrics, congestion maps, power estimates, and timing constraints to create a comprehensive dataset for training the models.

Model Training and Validation: Developing ML models that can effectively optimize multiple objectives simultaneously requires robust training and validation processes. Techniques such as ensemble learning, reinforcement learning, or multi-objective optimization algorithms can be explored to train models that can handle the complexity of the placement problem.

Interpretability and Transparency: Ensuring the interpretability and transparency of the ML models is essential for understanding how they make decisions and recommendations. Techniques such as feature importance analysis, model explainability tools, and visualization methods can help in interpreting the results and gaining insights into the optimization process.

Scalability and Efficiency: ML-based optimization techniques should be scalable to handle large-scale designs and efficient in terms of computational resources. Implementing parallel processing, distributed computing, or hardware acceleration can improve the scalability and efficiency of the optimization process.

Integration with Existing Framework: Integrating ML-based optimization techniques seamlessly into the DG-RePlAce framework while maintaining compatibility with existing algorithms and workflows is critical. This involves designing a flexible and modular architecture that allows for easy integration and testing of new optimization methods.

By addressing these challenges through careful design, implementation, and validation, the incorporation of ML-based multi-objective optimization techniques into the DG-RePlAce framework can lead to significant improvements in placement quality and efficiency.

How can the physical hierarchy extraction process be further optimized to reduce the overall turnaround time of DG-RePlAce, especially for large-scale designs?

Optimizing the physical hierarchy extraction process is essential to reduce the overall turnaround time of DG-RePlAce, particularly for large-scale designs. Here are some strategies to enhance the efficiency of the physical hierarchy extraction:

Algorithmic Improvements: Develop more efficient algorithms for physical hierarchy extraction that can handle large-scale designs with reduced computational complexity. This may involve optimizing clustering techniques, graph traversal algorithms, or partitioning methods to speed up the extraction process.

Parallel Processing: Implement parallel processing techniques to distribute the workload across multiple cores or nodes, enabling faster extraction of the physical hierarchy. Utilizing parallelism can significantly reduce the processing time for analyzing and organizing the design hierarchy.

Incremental Processing: Adopt an incremental processing approach where the physical hierarchy extraction is performed in stages, focusing on specific regions or clusters of the design at a time. This incremental processing can help in managing memory usage and processing overhead more efficiently.

Caching and Memoization: Implement caching mechanisms to store intermediate results and avoid redundant computations during the physical hierarchy extraction process. By caching previously processed data, the system can retrieve information quickly and accelerate subsequent extraction steps.

Hardware Acceleration: Explore the use of hardware acceleration techniques, such as FPGA or GPU acceleration, to offload computationally intensive tasks involved in physical hierarchy extraction. Hardware acceleration can significantly speed up the processing of large-scale designs.

Optimized Data Structures: Design and implement optimized data structures that are tailored for the physical hierarchy extraction process. Efficient data structures can improve memory access patterns, reduce data retrieval times, and enhance overall processing speed.

By incorporating these optimization strategies into the physical hierarchy extraction process of DG-RePlAce, it is possible to streamline the extraction of design hierarchy information, leading to faster turnaround times and improved overall efficiency for large-scale designs.