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On the Acceleration of Deep Neural Network Inference using Quantized Compressed Sensing

arXiv 2108.10101 ↗First posted 23 Aug 2021cs.LG cs.NA cs.NEcited by 0

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Abstract (arXiv)

Accelerating deep neural network (DNN) inference on resource-limited devices is one of the most important barriers to ensuring a wider and more inclusive adoption. To alleviate this, DNN binary quantization for faster convolution and memory savings is one of the most promising strategies despite its serious drop in accuracy. The present paper therefore proposes a novel binary quantization function based on quantized compressed sensing (QCS). Theoretical arguments conjecture that our proposal preserves the practical benefits of standard methods, while reducing the quantization error and the resulting drop in accuracy.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 18 references that are in the map. First posted on arXiv 23 Aug 2021.

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11 of 18 references.

References · 11

  1. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
  2. 2014Going Deeper with Convolutionscited by 4,241
  3. 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,332
  4. 2017Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inferencecited by 1,593
  5. 2017Learning Transferable Architectures for Scalable Image Recognitioncited by 1,101
  6. 2016XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networkscited by 849
  7. 2018HAQ: Hardware-Aware Automated Quantization with Mixed Precisioncited by 443
  8. 2015Deep Learning with Limited Numerical Precisioncited by 376
  9. 2014Speeding up Convolutional Neural Networks with Low Rank Expansionscited by 284
  10. 2020Binary Neural Networks: A Surveycited by 190
  11. 2020Least squares binary quantization of neural networkscited by 21

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  1. 2025SQS: Bayesian DNN Compression through Sparse Quantized Sub-distributions
  2. 2022A Comprehensive Survey on Model Quantization for Deep Neural Networks in Image Classification
  3. 2024Efficient Neural Compression with Inference-time Decoding
  4. 2021Quantized Neural Networks via {-1, +1} Encoding Decomposition and Acceleration
  5. 2021HEMP: High-order Entropy Minimization for neural network comPression
  6. 2021Hardware-friendly Deep Learning by Network Quantization and Binarization
  7. 2021Neural Network Quantization for Efficient Inference: A Survey
  8. 2023Hardware-Aware DNN Compression via Diverse Pruning and Mixed-Precision Quantization