Neural Network Chip Placement for Faster Floorplan Generation
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Solution Overview
Problem
Conventional floorplanning solutions for computer chip design require extensive human intervention and consume significant computational resources, struggling to generate high-quality chip placements efficiently.
Innovation Solution
A system utilizing a node placement neural network trained through reinforcement learning to automatically generate high-quality chip floorplans, allowing for rapid generation of optimal placements with minimal user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional automated floorplanning approaches are used, then computational power and time are consumed, but high-quality floorplans cannot be reliably generated without excessive computational resources and human involvement
Solution Approach 1:
The system employs reinforcement learning to enable the floorplanning algorithm to automatically learn and optimize placement strategies without requiring human expert intervention. The neural network autonomously improves its floorplan generation capabilities through iterative training, eliminating the need for manual guidance while reducing computational overhead compared to traditional automated approaches.
Solution Approach 2:
The invention transforms the floorplanning problem by changing the approach from traditional deterministic algorithms to probabilistic neural network-based decision making. The system uses learned probability distributions to guide placement decisions, allowing it to efficiently explore the solution space and generate high-quality floorplans with significantly reduced computational resources compared to exhaustive search methods.
2Manufacturing precision
If conventional floorplanning methods are used, then human expert involvement is required, but the process becomes weeks long and computationally expensive
Solution Approach 1:
The reinforcement learning model enables the system to automatically achieve expert-level floorplan quality without actual human experts being involved in the generation process. The neural network learns from training data to produce high-quality placements autonomously, reducing the weeks-long process to hours while maintaining or improving floorplan quality metrics.
Solution Approach 2:
The system performs preliminary learning and optimization during the training phase, where the neural network learns effective floorplanning strategies from example solutions. This preliminary action allows the model to quickly generate high-quality floorplans during inference without requiring time-consuming manual adjustment or iterative optimization during the actual design process.
3Reliability
If traditional automated approaches are used, then extensive computational resources are consumed, but high-quality floorplans cannot be reliably generated
Solution Approach 1:
The invention replaces traditional mechanical search and optimization algorithms with a neural network-based system that uses learned patterns and probability distributions. This substitution allows the system to reliably generate high-quality floorplans by leveraging learned knowledge rather than exhaustive computational search, significantly reducing computational resource consumption while improving consistency.
Solution Approach 2:
The reinforcement learning framework incorporates feedback mechanisms where the neural network receives reward signals based on floorplan quality metrics. This feedback loop allows the system to continuously improve its placement strategies, learning from successful placements and adjusting its policy to consistently generate high-quality floorplans with reliable performance across different design scenarios.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a computer chip placement. One of the methods includes obtaining netlist data for a computer chip; and generating a computer chip placement, comprising placing a respective macro node at each time step in a sequence comprising a plurality of time steps, the placing comprising, for each time step: generating an input representation for the time step; processing the input representation using a node placement neural network having a plurality of network parameters, wherein the node placement neural network is configured to process the input representation in accordance with current values of the network parameters to generate a score distribution over a plurality of positions on the surface of the computer chip; and assigning the macro node to be placed at the time step to a position from the plurality of positions using the score distribution.