mlsanityData to 13 Oct 2026

AMC: AutoML for Model Compression and Acceleration on Mobile Devices

arXiv 1802.03494 ↗First posted 10 Feb 2018cs.CVcited by 377

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Abstract (arXiv)

Model compression is a critical technique to efficiently deploy neural network models on mobile devices which have limited computation resources and tight power budgets. Conventional model compression techniques rely on hand-crafted heuristics and rule-based policies that require domain experts to explore the large design space trading off among model size, speed, and accuracy, which is usually sub-optimal and time-consuming. In this paper, we propose AutoML for Model Compression (AMC) which leverage reinforcement learning to provide the model compression policy. This learning-based compression policy outperforms conventional rule-based compression policy by having higher compression ratio, better preserving the accuracy and freeing human labor. Under 4x FLOPs reduction, we achieved 2.7% better accuracy than the handcrafted model compression policy for VGG-16 on ImageNet. We applied this automated, push-the-button compression pipeline to MobileNet and achieved 1.81x speedup of measured inference latency on an Android phone and 1.43x speedup on the Titan XP GPU, with only 0.1% loss of ImageNet Top-1 accuracy.

Key facts. Cited by 377 papers in the map (data through 13 Oct 2026). First posted on arXiv 10 Feb 2018.

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Citing papers per year: 2021: 131; 2022: 77; 2023: 66; 2024: 42; 2025: 42; 2026: 19.

377 papers.

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  11. 2021CDFI: Compression-Driven Network Design for Frame Interpolationcited by 55
  12. 2024Survey on Knowledge Distillation for Large Language Models: Methods, Evaluation, and Applicationcited by 55
  13. 2021Anycost GANs for Interactive Image Synthesis and Editingcited by 54
  14. 2021A Survey on Green Deep Learningcited by 52
  15. 2024Efficient Expert Pruning for Sparse Mixture-of-Experts Language Models: Enhancing Performance and Reducing Inference Costscited by 52
  16. 2021How Well Do Sparse Imagenet Models Transfer?cited by 50
  17. 2022Revisiting Random Channel Pruning for Neural Network Compressioncited by 50
  18. 2021FedGEMS: Federated Learning of Larger Server Models via Selective Knowledge Fusioncited by 49
  19. 2023ZipLM: Inference-Aware Structured Pruning of Language Modelscited by 46
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  26. 2022NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Resultscited by 40
  27. 2022Structural Pruning via Latency-Saliency Knapsackcited by 40
  28. 2024Tiny Machine Learning: Progress and Futurescited by 40
  29. 2021Towards Compact CNNs via Collaborative Compressioncited by 36
  30. 2022CHEX: CHannel EXploration for CNN Model Compressioncited by 36
  31. 2022SPDY: Accurate Pruning with Speedup Guaranteescited by 34
  32. 2021AutoDS: Towards Human-Centered Automation of Data Sciencecited by 32
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  38. 2021SOSP: Efficiently Capturing Global Correlations by Second-Order Structured Pruningcited by 30

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