DG-RePlAce: Advancing Global Placement for Machine Learning Chips

Global placement is an important step in designing integrated circuits. It involves deciding where to position various components on a chip. As machine learning accelerators become more popular, new challenges have arisen that make this process more complex.

This article discusses a new tool called DG-RePlAce. It is designed to improve placement tasks specifically for machine learning accelerators. By using advanced computing techniques, DG-RePlAce offers better results in terms of placement efficiency and performance.

#Background on Global Placement

In chip design, global placement helps determine the layout of standard cells and macros. A fast placement engine is required for quick design iterations. Traditional methods often struggle with large machine learning accelerators that contain millions of components. This can slow down the design process significantly.

Emerging technologies that rely on 2D processing elements have introduced new structure and data flow. These factors are important to consider during placement for achieving better results.

#Features of DG-RePlAce

DG-RePlAce builds on the OpenROAD framework. It takes advantage of the unique structures found in machine learning accelerators. Compared to existing tools like RePlAce and DREAMPlace, DG-RePlAce shows impressive improvements in placement quality and overall speed.

• Dataflow and Datapath Structures: DG-RePlAce utilizes the internal workings of machine learning designs. By understanding how data moves within these systems, it can make smarter placement decisions.

• GPU Acceleration: The tool takes advantage of graphics processing units (GPUs) to run faster. This feature allows for parallel processing, which speeds up calculations significantly.

• Enhanced Algorithms: The team behind DG-RePlAce has developed new algorithms for calculating wire lengths and placement metrics. These enhancements lead to quicker convergence and improved runtime.

#Process of Using DG-RePlAce

DG-RePlAce works through several steps. First, it takes a synthesized netlist, which is a structural representation of the design along with a floor plan file. The tool processes this information through a series of methods.

1. Physical Hierarchy Extraction: In this phase, the tool organizes the components into clusters based on their connections. This step ensures that related components remain close during placement.

2. Dataflow-Driven Initial Distribution: Here, DG-RePlAce incorporates dataflow information into the clustered setup. It determines initial positions for these clusters using parallel computation.

3. Constructing Datapath Constraints: The next step involves extracting detailed data movement information from the netlist. This information helps refine placement decisions further.

4. Parallel Analytical Placement: Finally, the tool performs a complete placement analysis using the generated constraints. The use of GPUs allows for rapid processing, resulting in efficient, high-quality placements.

#Results and Performance Metrics

In tests against other placement tools like RePlAce and DREAMPlace, DG-RePlAce boasts significant advantages.

• Wirelength Reduction: The tool reduces the total length of wires used to connect components, which generally leads to better performance.

• Timing Improvements: DG-RePlAce achieves better timing metrics, meaning the signals can travel through the chip more quickly, enhancing the overall speed of the design.

• Efficiency: Despite its advanced features, DG-RePlAce matches the total runtime of its competitors while performing placement significantly faster.

The performance was validated across various machine learning designs, showcasing DG-RePlAce’s capabilities in diverse environments.

#Insights from Experimental Studies

As part of its evaluation, DG-RePlAce was tested on a range of benchmarks. The results reveal that employing dataflow and datapath structures can lead to substantial improvements in performance.

• Testing Benchmarks: The tool was assessed against well-known benchmarks like Tabla and GeneSys designs. These tests highlighted DG-RePlAce's ability to optimize placements effectively.

• Ablation Studies: By removing dataflow or datapath constraints, researchers found that both elements play a crucial role in enhancing placement quality. Each variant showed that having these constraints yields better overall results.

#Runtime Efficiency Comparison

The runtime efficiency of DG-RePlAce stands out when compared to DREAMPlace.

• Reduced Iterations: The tool required fewer iterations to reach convergence. This efficiency can be attributed to the insights gained during the initial distribution phase.

• Faster Computation: DG-RePlAce's algorithms for calculating wire length and density are optimized for speed, allowing it to perform better with larger designs.

Though DG-RePlAce's overall turnaround time may be higher due to certain file operations, its core placement runtime is significantly faster than that of its peers. This makes it suitable for scenarios where placement tasks are repeated multiple times.

#Perspectives on Future Work

The development team has identified several areas for improvement and exploration:

1. Incorporation of Density Screens: By adding features to manage density, DG-RePlAce can enhance its routability further.

2. Machine Learning Integration: Future plans include using machine learning techniques to optimize the tool’s hyperparameters for even better trade-offs between various performance metrics.

3. Streamlining Hierarchy Extraction: This process is currently a bottleneck. Optimizing it could improve the overall efficiency of DG-RePlAce.

#Conclusion

DG-RePlAce demonstrates that leveraging the unique characteristics of machine learning accelerators can lead to substantial gains in the global placement process. Its enhancements over traditional placement tools highlight the potential to optimize designs more efficiently.

The tool not only meets the demands of modern machine learning hardware but also lays the groundwork for future developments in placement methodologies. With continued improvements, DG-RePlAce promises to be a valuable asset in the field of integrated circuit design, particularly for machine learning applications.