# RESPECT: Reinforcement Learning based Edge Scheduling on Pipelined Coral Edge TPUs

arXiv: https://arxiv.org/abs/2304.04716 · HTML: https://mlsanity.com/paper/2304.04716/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 25 references that are in the map.
- First posted on arXiv 10 Apr 2023.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015)
- [Attention, Learn to Solve Routing Problems!](https://mlsanity.com/paper/1803.08475/) (2018)
- [Neural Combinatorial Optimization with Reinforcement Learning](https://mlsanity.com/paper/1611.09940/) (2016)
- [Learning Scheduling Algorithms for Data Processing Clusters](https://mlsanity.com/paper/1810.01963/) (2018)
- [An Evaluation of Edge TPU Accelerators for Convolutional Neural Networks](https://mlsanity.com/paper/2102.10423/) (2021)
- [Developing Synthesis Flows Without Human Knowledge](https://mlsanity.com/paper/1804.05714/) (2018)
- [Ordering Chaos: Memory-Aware Scheduling of Irregularly Wired Neural Networks for Edge Devices](https://mlsanity.com/paper/2003.02369/) (2020)
- [Painting on Placement: Forecasting Routing Congestion using Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1904.07077/) (2019)
- [Mitigating Edge Machine Learning Inference Bottlenecks: An Empirical Study on Accelerating Google Edge Models](https://mlsanity.com/paper/2103.00768/) (2021)

## Built on by (most cited first, 7 of 7)

- [Differentiable Combinatorial Scheduling at Scale](https://mlsanity.com/paper/2406.06593/) (2024)
- [Verilog-to-PyG -- A Framework for Graph Learning and Augmentation on RTL Designs](https://mlsanity.com/paper/2311.05722/) (2023)
- [Accelerating Exact Combinatorial Optimization via RL-based Initialization -- A Case Study in Scheduling](https://mlsanity.com/paper/2308.11652/) (2023)
- [Rubik's Optical Neural Networks: Multi-task Learning with Physics-aware Rotation Architecture](https://mlsanity.com/paper/2304.12985/) (2023)
- [BoolGebra: Attributed Graph-learning for Boolean Algebraic Manipulation](https://mlsanity.com/paper/2401.10753/) (2024)
- [e-boost: Boosted E-Graph Extraction with Adaptive Heuristics and Exact Solving](https://mlsanity.com/paper/2508.13020/) (2025)
- [Differentiable Initialization-Accelerated CPU-GPU Hybrid Combinatorial Scheduling](https://mlsanity.com/paper/2603.28943/) (2026)

## Abstract (arXiv)

Deep neural networks (DNNs) have substantial computational and memory requirements, and the compilation of its computational graphs has a great impact on the performance of resource-constrained (e.g., computation, I/O, and memory-bound) edge computing systems. While efficient execution of their computational graph requires an effective scheduling algorithm, generating the optimal scheduling solution is a challenging NP-hard problem. Furthermore, the complexity of scheduling DNN computational graphs will further increase on pipelined multi-core systems considering memory communication cost, as well as the increasing size of DNNs. Using the synthetic graph for the training dataset, this work presents a reinforcement learning (RL) based scheduling framework RESPECT, which learns the behaviors of optimal optimization algorithms and generates near-optimal scheduling results with short solving runtime overhead. Our framework has demonstrated up to ~2.5× real-world on-chip inference runtime speedups over the commercial compiler with ten popular ImageNet models deployed on the physical Coral Edge TPUs system. Moreover, compared to the exact optimization methods, the proposed RL scheduling improves the scheduling optimization runtime by up to 683× speedups compared to the commercial compiler and matches the exact optimal solutions with up to 930× speedups. Finally, we perform a comprehensive generalizability test, which demonstrates RESPECT successfully imitates optimal solving behaviors from small synthetic graphs to large real-world DNNs computational graphs.