Hierarchical Memory-Constrained Operator Scheduling of Neural Architecture Search Networks
TimeWednesday, July 13th10:30am - 10:52am PDT
Location3000, Level 3
ML Algorithms and Applications
DescriptionNeural Architecture Search (NAS) is widely used in industry, searching for neural networks meeting task requirements. Meanwhile, it faces a challenge in scheduling networks satisfying memory constraints. This paper proposes HMCOS that performs hierarchical memory-constrained operator scheduling of NAS networks: given a network, HMCOS constructs a hierarchical computation graph and employs an iterative scheduling algorithm to progressively reduce peak memory footprints. We evaluate HMCOS against RPO and Serenity (two popular techniques). The results show that HMCOS outperforms existing techniques in supporting more NAS networks, reducing 8.7~42.4% of peak memory footprints, and achieving 137~283x of speedups in scheduling.