Scheduling on dataflow graphs (also known as computation graphs) is an NP-hard problem. The traditional exact methods are limited by runtime complexity, while reinforcement learning (RL) and heuristic-based approaches struggle with determinism and solution quality. This research aims to develop an innovative approach that employs machine learning (ML) for addressing combinatorial optimization problems, using scheduling as a case study. The goal is to provide guarantees in optimality and determinism while maintaining the runtime cost of heuristic methods. Specifically, we introduce a novel two-phase RL-to-ILP scheduling framework, which includes three steps: 1) RL solver acts as coarse-grain scheduler, 2) solution relaxation and 3) exact solving via ILP. Our framework demonstrates the same scheduling performance compared with using exact scheduling methods while achieving up to 128 $\times$ speed improvements. This was conducted on actual EdgeTPU platforms, utilizing ImageNet DNN computation graphs as input. Additionally, the framework offers improved on-chip inference runtime and acceleration compared to the commercially available EdgeTPU compiler.

翻译：数据流图（即计算图）上的调度是NP难问题。传统精确方法受限于运行时间复杂度，而强化学习（RL）和基于启发式的方法则难以兼顾确定性与解的质量。本研究旨在开发一种创新方法，采用机器学习（ML）解决组合优化问题，并以调度为案例。目标是同时保证最优性（optimality）和确定性（determinism），同时维持启发式方法的运行时间开销。具体而言，我们提出了一种新颖的两阶段RL-to-ILP调度框架，包含三个步骤：1）RL求解器作为粗粒度调度器，2）解松弛，以及3）通过整数线性规划（ILP）进行精确求解。与使用精确调度方法相比，我们的框架展现了相同的调度性能，同时实现了高达128倍的加速。该实验在实际的EdgeTPU平台上进行，并以ImageNet深度神经网络计算图作为输入。此外，与商用EdgeTPU编译器相比，该框架还提供了改进的片上推理运行时和加速效果。