mlsanityData to 7 Oct 2026

Compilation and Optimizations for Efficient Machine Learning on Embedded Systems

arXiv 2206.03326 ↗First posted 6 Jun 2022cs.LG cs.ARcited by 0

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Abstract (arXiv)

Deep Neural Networks (DNNs) have achieved great success in a variety of machine learning (ML) applications, delivering high-quality inferencing solutions in computer vision, natural language processing, and virtual reality, etc. However, DNN-based ML applications also bring much increased computational and storage requirements, which are particularly challenging for embedded systems with limited compute/storage resources, tight power budgets, and small form factors. Challenges also come from the diverse application-specific requirements, including real-time responses, high-throughput performance, and reliable inference accuracy. To address these challenges, we introduce a series of effective design methodologies, including efficient ML model designs, customized hardware accelerator designs, and hardware/software co-design strategies to enable efficient ML applications on embedded systems.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 71 of its 122 references that are in the map. First posted on arXiv 6 Jun 2022.

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71 of 122 references.

References · 71

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  4. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  5. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  6. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,773
  7. 2014Going Deeper with Convolutionscited by 4,231
  8. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,827
  9. 2016Categorical Reparameterization with Gumbel-Softmaxcited by 2,708
  10. 2015Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Codingcited by 2,291
  11. 2015Learning both Weights and Connections for Efficient Neural Networkscited by 1,998
  12. 2018DARTS: Differentiable Architecture Searchcited by 1,782
38 more
  1. 2016Neural Architecture Search with Reinforcement Learningcited by 1,489
  2. 2016YOLO9000: Better, Faster, Strongercited by 1,429
  3. 2018ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Designcited by 1,132
  4. 2017Learning Transferable Architectures for Scalable Image Recognitioncited by 1,097
  5. 2018MnasNet: Platform-Aware Neural Architecture Search for Mobilecited by 937
  6. 2016XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networkscited by 829
  7. 2019Once-for-All: Train One Network and Specialize it for Efficient Deploymentcited by 778
  8. 2018ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardwarecited by 666
  9. 2017In-Datacenter Performance Analysis of a Tensor Processing Unitcited by 610
  10. 2015BinaryConnect: Training Deep Neural Networks with binary weights during propagationscited by 571
  11. 2016DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradientscited by 557
  12. 2018FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Searchcited by 480
  13. 2017ThiNet: A Filter Level Pruning Method for Deep Neural Network Compressioncited by 438
  14. 2016EIE: Efficient Inference Engine on Compressed Deep Neural Networkcited by 291
  15. 2018SNAS: Stochastic Neural Architecture Searchcited by 288
  16. 2014Caffe: Convolutional Architecture for Fast Feature Embeddingcited by 287
  17. 2016Designing Neural Network Architectures using Reinforcement Learningcited by 277
  18. 2020MCUNet: Tiny Deep Learning on IoT Devicescited by 269
  19. 2019Random Search and Reproducibility for Neural Architecture Searchcited by 253
  20. 2020Neural Architecture Search without Trainingcited by 241
  21. 2017Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weightscited by 211
  22. 2016Trained Ternary Quantizationcited by 203
  23. 2016Ternary Weight Networkscited by 195
  24. 2021Zero-Cost Proxies for Lightweight NAScited by 194
  25. 2018Deep Appearance Models for Face Renderingcited by 164
  26. 2018Neural Architecture Optimizationcited by 164
  27. 2016FINN: A Framework for Fast, Scalable Binarized Neural Network Inferencecited by 141
  28. 2019Finding Task-Relevant Features for Few-Shot Learning by Category Traversalcited by 101
  29. 2020PatDNN: Achieving Real-Time DNN Execution on Mobile Devices with Pattern-based Weight Pruningcited by 75
  30. 2017Extremely Low Bit Neural Network: Squeeze the Last Bit Out with ADMMcited by 60
  31. 2017NeST: A Neural Network Synthesis Tool Based on a Grow-and-Prune Paradigmcited by 60
  32. 2021A Comprehensive Survey on Hardware-Aware Neural Architecture Searchcited by 60
  33. 2019Hardware/Software Co-Exploration of Neural Architecturescited by 50
  34. 2020Few-shot Neural Architecture Searchcited by 50
  35. 2019FPGA/DNN Co-Design: An Efficient Design Methodology for IoT Intelligence on the Edgecited by 48
  36. 2019PCONV: The Missing but Desirable Sparsity in DNN Weight Pruning for Real-time Execution on Mobile Devicescited by 48
  37. 2015Neural Networks with Few Multiplicationscited by 46
  38. 2020Co-Exploration of Neural Architectures and Heterogeneous ASIC Accelerator Designs Targeting Multiple Taskscited by 43

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