LSTM-Characterized Approach for Chip Floorplanning: Leveraging HyperGCN and DRQN

In the field of very large-scale integration (VLSI) chip design, chip floorplanning plays a crucial role as it directly influences key optimization objectives such as placement wirelength. This, in turn, affects signal delay, power efficiency, routability, and overall cost. However, traditional reinforcement learning (RL) methods for chip floorplanning often oversimplify this complex and dynamic task. They tend to overlook the cascading effects of module placements and fail to fully comprehend the intricate interdependencies that are vital for making informed decisions. To address these challenges, we introduce an innovative approach that combines hypergraph graph convolutional networks (HyperGCNs) with deep recurrent Q-networks (DRQNs). This integration allows us to capture the nuanced dynamics and interconnected aspects of chip design more effectively. We enhance the traditional Markov decision process (MDP) model by incorporating a state characterization layer based on long short-term memory (LSTM) technology. Initially, HyperGCN efficiently encodes netlist information, simplifying complex graph structures into lower dimensional vectors, thereby enhancing knowledge processing. Subsequently, we treat the chip as an agent and apply the DRQN algorithm to optimize the module layout. DRQN’s LSTM utilizes a recurrent layer structure to grasp dependencies between modules, combined with the deep Q-network (DQN)’s optimization capabilities, enabling us to navigate the complexities of floorplanning. Our approach improves the state representation by encompassing a broader understanding of interconnected module characteristics. This allows our agents to make decisions that take into account the collective impact of module adjustments, rather than viewing each change in isolation. This comprehensive state representation, which includes diverse features and their evolving relationships, significantly enhances the decision-making capabilities of our agents. Our extensive experiments demonstrate that our method outperforms traditional heuristic-based and other learning-based floorplanning techniques. To the best of our knowledge, this is the first application of LSTM with DQN in circuit design, representing a significant advancement in the field.