mlsanityData to 14 Oct 2026

Automated Heterogeneous Low-Bit Quantization of Multi-Model Deep Learning Inference Pipeline

arXiv 2311.05870 ↗First posted 10 Nov 2023cs.CV math.OCcited by 1

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Abstract (arXiv)

Multiple Deep Neural Networks (DNNs) integrated into single Deep Learning (DL) inference pipelines e.g. Multi-Task Learning (MTL) or Ensemble Learning (EL), etc., albeit very accurate, pose challenges for edge deployment. In these systems, models vary in their quantization tolerance and resource demands, requiring meticulous tuning for accuracy-latency balance. This paper introduces an automated heterogeneous quantization approach for DL inference pipelines with multiple DNNs.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 8 of its 12 references that are in the map. First posted on arXiv 10 Nov 2023.

Builds on

8 of 12 references.

References · 8

  1. 2016XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networkscited by 849
  2. 2020MediaPipe Hands: On-device Real-time Hand Trackingcited by 161
  3. 2020Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectorscited by 80
  4. 2021Towards Mixed-Precision Quantization of Neural Networks via Constrained Optimizationcited by 61
  5. 2016Bitwise Neural Networkscited by 29
  6. 2018Deep Neural Network Compression with Single and Multiple Level Quantizationcited by 27
  7. 2021RMSMP: A Novel Deep Neural Network Quantization Framework with Row-wise Mixed Schemes and Multiple Precisionscited by 6
  8. 2023Computational Solar Energy -- Ensemble Learning Methods for Prediction of Solar Power Generation based on Meteorological Parameters in Eastern Indiacited by 3

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Citing papers per year: 2023: 1.

1 paper.

  1. 2023Local Masking Meets Progressive Freezing: Crafting Efficient Vision Transformers for Self-Supervised Learningcited by 1

Similar, not cited

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  1. 2026SigmaQuant: Hardware-Aware Heterogeneous Quantization Method for Edge DNN Inference
  2. 2022Edge Inference with Fully Differentiable Quantized Mixed Precision Neural Networks
  3. 2025Low-bit Model Quantization for Deep Neural Networks: A Survey
  4. 2022Vertical Layering of Quantized Neural Networks for Heterogeneous Inference
  5. 2025Dual Precision Quantization for Efficient and Accurate Deep Neural Networks Inference
  6. 2023Performance Characterization of using Quantization for DNN Inference on Edge Devices: Extended Version
  7. 2025SplitQuantV2: Enhancing Low-Bit Quantization of LLMs Without GPUs
  8. 2022A Comprehensive Survey on Model Quantization for Deep Neural Networks in Image Classification