Hyperparameter Partitioning for Deep Reinforcement Learning IC Design
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Solution Overview
Problem
Current deep reinforcement learning methods for integrated circuit design face challenges with large-sized netlists, leading to increased computational costs and difficulties in learning and inference due to the size of the netlist, which results in inefficient design processes and non-uniform mass-produced goods.
Innovation Solution
A deep reinforcement learning-based integrated circuit design system using partitioning that reduces the size of the netlist by performing parameterized hyperparameter partitioning, balancing partition sizes, and preserving the properties of the hypergraph, thereby reducing the computational load and capacity of the artificial neural network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If deep reinforcement learning is applied to large-sized netlists for integrated circuit design, then design automation and optimization are improved, but computational costs and network capacity requirements increase significantly
Solution Approach 1:
The patent divides the large netlist into multiple smaller sub-netlists through partitioning. Each sub-netlist is processed independently by the reinforcement learning agent, reducing the computational load per iteration while maintaining overall design optimization. This segmentation allows the system to handle large circuits without requiring proportional increases in computational resources.
Solution Approach 2:
The patent extracts and removes redundant or less critical components from the netlist representation. By identifying and excluding unnecessary elements, the system reduces the effective size of the input data for reinforcement learning, thereby lowering computational costs while preserving the essential design information needed for optimization.
2Use of energy by moving object
If the size of the netlist is reduced through partitioning, then computational load and network capacity requirements are decreased, but the complexity of the partitioning process increases
Solution Approach 1:
The patent performs preliminary analysis and classification of circuit components before partitioning. By pre-identifying critical paths, high-fan-out nodes, and other important elements, the system can guide the partitioning process to create meaningful sub-netlists. This preliminary action simplifies the actual partitioning by providing clear criteria for division, reducing the overall complexity.
Solution Approach 2:
The patent changes the representation parameters of the netlist during partitioning. By transforming the netlist into a format that emphasizes structural relationships rather than detailed connectivity, the system simplifies the partitioning process. Parameter transformations such as aggregating similar components or reweighting connections make the partitioning more straightforward and computationally efficient.
3Adaptability or versatility
If manual design methods are used for integrated circuit layout, then design flexibility and customization are maintained, but work time and human resources increase significantly
Solution Approach 1:
The patent implements a feedback loop where the reinforcement learning agent continuously evaluates placement solutions and adjusts its actions accordingly. The agent receives reward signals based on design quality metrics and uses this feedback to iteratively improve the layout. This feedback mechanism enables automated systems to achieve design flexibility comparable to manual methods while dramatically reducing the time and effort required.
Solution Approach 2:
The reinforcement learning agent performs self-guided optimization without requiring human intervention for each design decision. The agent autonomously explores the design space, learns from its own actions, and generates customized layouts that adapt to specific requirements. This self-service capability maintains design flexibility while eliminating the need for continuous human input, thereby significantly improving productivity.
Data Source
AI summary
The present disclosure may provide parameterized hyperparameter partitioning in consideration of balance in partition size while preserving a property of a hypergraph necessary to apply deep reinforcement learning by reducing the large-size hypergraph, and may reduce the computational amount and capacity of an artificial neural network by reducing a graph.