Summary
NVIDIA’s AutoDMP is a groundbreaking methodology that combines GPU-accelerated computing with AI to improve chip design and increase semiconductor performance. By leveraging AI/ML multi-objective parameter optimization, AutoDMP accelerates the process of macro placement, a critical step in chip design that impacts power-performance-area (PPA) metrics. This article explores how AutoDMP works, its benefits, and its potential to revolutionize chip design.
The Challenge of Chip Design
Chip design is a complex process that involves placing millions of components, such as transistors and memory macros, on a tiny piece of silicon. The placement of these components, known as macro placement, has a significant impact on the performance, power consumption, and area of the chip. Traditional methods of macro placement rely on manual tuning and heuristic-driven optimizations, which can be time-consuming and unpredictable.
How AutoDMP Works
AutoDMP addresses the challenge of macro placement by combining GPU-accelerated computing with AI/ML multi-objective parameter optimization. The process involves two main steps:
- Multi-objective parameter optimization: AutoDMP uses a multi-objective hyperparameter optimization technique to find a set of placements whose estimated wire length, congestion, and density lie on the Pareto front. This step maps the design space of the AutoDMP parameters to the objective proxy space.
- Mapping to EDA tool’s real PPA space: The macro placements on the objective space Pareto front are then mapped to the EDA tool’s real PPA space. The EDA tool conducts numerous optimizations of the placement, many of which are heuristic-driven and difficult to predict.
Benefits of AutoDMP
AutoDMP offers several benefits over traditional methods of macro placement:
- Speed: AutoDMP can optimize a design with 2.7 million cells and 320 macros in just 3 hours on a single NVIDIA DGX Station A100.
- Accuracy: AutoDMP’s PPA metrics are equal to or better than the commercial flow.
- Efficiency: AutoDMP is computationally efficient, allowing for faster design space exploration.
Real-World Applications
AutoDMP has been tested on real-world designs, including a 256 RISC-V core design with 2.7 million standard cells and 320 memory macros. The results show that AutoDMP can achieve optimal layouts in a fraction of the time required by traditional methods.
The Future of Chip Design
AutoDMP has the potential to revolutionize chip design by enabling faster and more accurate macro placement. By combining GPU-accelerated computing with AI/ML multi-objective parameter optimization, AutoDMP can unlock new prospective design space exploration techniques. This can lead to better PPA metrics, faster time-to-market, and more efficient chip design.
Table: Comparison of AutoDMP with Traditional Methods
| Method | Time | PPA Metrics |
|---|---|---|
| Traditional | Days/Weeks | Unpredictable |
| AutoDMP | 3 hours | Equal to or better than commercial flow |
Table: Benefits of AutoDMP
| Benefit | Description |
|---|---|
| Speed | Optimizes design in 3 hours |
| Accuracy | PPA metrics equal to or better than commercial flow |
| Efficiency | Computationally efficient, allowing for faster design space exploration |
Conclusion
NVIDIA’s AutoDMP is a groundbreaking methodology that combines GPU-accelerated computing with AI to improve chip design and increase semiconductor performance. By leveraging AI/ML multi-objective parameter optimization, AutoDMP accelerates the process of macro placement, a critical step in chip design that impacts power-performance-area (PPA) metrics. With its speed, accuracy, and efficiency, AutoDMP has the potential to revolutionize chip design and enable faster and more efficient design space exploration.