Chiplet Placement Order Exploration Based on Learning to Rank with Graph Representation
Zhihui Deng, Yuanyuan Duan, Leilai Shao, Xiaolei Zhu

TL;DR
This paper introduces a graph-based learning to rank method to optimize chiplet placement order, significantly improving system temperature and wirelength in chiplet-based systems.
Contribution
It proposes a novel learning to rank approach with graph representation to determine optimal chiplet placement order, enhancing reinforcement learning placement strategies.
Findings
10.05% reduction in inter-chiplet wirelength
1.01% improvement in peak system temperature
outperforms traditional ordering methods
Abstract
Chiplet-based systems, integrating various silicon dies manufactured at different integrated circuit technology nodes on a carrier interposer, have garnered significant attention in recent years due to their cost-effectiveness and competitive performance. The widespread adoption of reinforcement learning as a sequential placement method has introduced a new challenge in determining the optimal placement order for each chiplet. The order in which chiplets are placed on the interposer influences the spatial resources available for earlier and later placed chiplets, making the placement results highly sensitive to the sequence of chiplet placement. To address these challenges, we propose a learning to rank approach with graph representation, building upon the reinforcement learning framework RLPlanner. This method aims to select the optimal chiplet placement order for each chiplet-based…
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Taxonomy
TopicsHandwritten Text Recognition Techniques · Manufacturing Process and Optimization
