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Memory-aware network scheduling is becoming increasingly important for deepneural network (DNN) inference on resource-constrained devices. However, due tothe complex cell-level and network-level topologies, memory-aware schedulingbecomes very challenging. While previous algorithms all suffer from poorscalability, in this paper, we propose an efficient memory-aware schedulingframework based on iterative computation graph optimization. Our frameworkfeatures an iterative graph fusion algorithm that simplifies the computationgraph while preserving the scheduling optimality. We further propose an integerlinear programming formulation together with topology-aware variable pruning toschedule the simplified graph efficiently. We evaluate our method againstprior-art algorithms on different networks and demonstrate that our methodoutperforms existing techniques in all the benchmarks, reducing the peak memoryfootprint by 13.4%, and achieving better scalability for networks with complexnetwork-level topologies.

Memory-aware Scheduling for Complex Wired Networks with Iterative Graph Optimization