SPHINX: Search Space-Pruning Heterogeneous Task Scheduling for Deep Neural Networks
Bowen Yuchi, Heng Shi, Guoqing Bao · 2024
Given the tendency of increasingly heterogeneous AI systems and the large workload scale of deep neural networks (DNNs), there is an urgent demand for model scheduling to improve execution performance in heterogeneous computational systems. However, this is very challenging because the task scheduling under the high-dimensional search space is an NP-hard problem. Existing works either schedule under naive search spaces without simplifications or oversimplifies the optimisation, which is hard to strike a balance between efficiency and optimality.