Memory-aware Scheduling for Complex Wired Networks with Iterative Graph
  Optimization
Shuzhang Zhong Meng Li Yun Liang Runsheng Wang Ru Huang
Memory-aware network scheduling is becoming increasingly important for deep
neural network (DNN) inference on resource-constrained devices. However, due to
the complex cell-level and network-level topologies, memory-aware scheduling
becomes very challenging. While previous algorithms all suffer from poor
scalability, in this paper, we propose an efficient memory-aware scheduling
framework based on iterative computation graph optimization. Our framework
features an iterative graph fusion algorithm that simplifies the computation
graph while preserving the scheduling optimality. We further propose an integer
linear programming formulation together with topology-aware variable pruning to
schedule the simplified graph efficiently. We evaluate our method against
prior-art algorithms on different networks and demonstrate that our method
outperforms existing techniques in all the benchmarks, reducing the peak memory
footprint by 13.4%, and achieving better scalability for networks with complex
network-level topologies.