SPLIDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate#

Murayyiam Parvez (Purdue), Annus Zulfiqar (Michigan), Roman Beltiukov (UCSB), Shir Landau Feibish (University of Haifa), Walter Willinger (Northwestern), Arpit Gupta (UCSB), Muhammad Shahbaz (Michigan)

• Why: In-network ML keeps stalling on the same wall — hardware limits force tiny fixed feature sets per flow. SPLIDT's partitioning trick is a real mechanism (not an ML benchmark bump) for getting past that wall, and it generalizes to any stateful line-rate inference problem on switches and SmartNICs.
• What: A system for deploying decision trees in the data plane that supports up to 5x more stateful features than prior approaches (NetBeacon, Leo) at the same low time-to-detection, scaling to millions of flows with ≤0.05% recirculation overhead.
• How: Two ideas: group DT subtrees into partitions where each subtree gets its own feature set (instead of computing all features upfront for the whole tree), and use in-band recirculation as a control channel to reuse register/match-key resources across partitions at line rate. A training + design-space-exploration framework jointly optimizes feature allocation, partitioning, and depth.
• Where applicable: Programmable switches and SmartNICs doing traffic classification, security monitoring, or in-network decisions. Artifact evaluated (available/functional/reproduced), so the partitioning approach is reproducible, not vendor-locked.