RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory

arXiv:2506.05994v3 Announce Type: replace-cross Abstract: Although deep learning has demonstrated remarkable capability in learning from unstructured data, modern tree-based ensemble models remain superior in extracting relevant information and learning from structured datasets. While several efforts have been made to accelerate tree-based models, the inherent characteristics of the models pose significant challenges for conventional accelerators. Recent research leveraging content-addressable memory (CAM) offers a promising solution for accelerating tree-based models, yet existing designs suffer from excessive memory consumption and low utilization.