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- W4386150557 abstract "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." @default.
- W4386150557 created "2023-08-26" @default.
- W4386150557 creator A5029321729 @default.
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- W4386150557 date "2023-08-19" @default.
- W4386150557 modified "2023-09-27" @default.
- W4386150557 title "Accelerating Exact Combinatorial Optimization via RL-based Initialization -- A Case Study in Scheduling" @default.
- W4386150557 doi "https://doi.org/10.48550/arxiv.2308.11652" @default.
- W4386150557 hasPublicationYear "2023" @default.
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