mlsanityData to 14 Oct 2026

BOMP-NAS: Bayesian Optimization Mixed Precision NAS

arXiv 2301.11810 ↗First posted 27 Jan 2023cs.LG cs.CVcited by 4

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Abstract (arXiv)

Bayesian Optimization Mixed-Precision Neural Architecture Search (BOMP-NAS) is an approach to quantization-aware neural architecture search (QA-NAS) that leverages both Bayesian optimization (BO) and mixed-precision quantization (MP) to efficiently search for compact, high performance deep neural networks. The results show that integrating quantization-aware fine-tuning (QAFT) into the NAS loop is a necessary step to find networks that perform well under low-precision quantization: integrating it allows a model size reduction of nearly 50% on the CIFAR-10 dataset. BOMP-NAS is able to find neural networks that achieve state of the art performance at much lower design costs. This study shows that BOMP-NAS can find these neural networks at a 6x shorter search time compared to the closest related work.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 16 of its 21 references that are in the map. First posted on arXiv 27 Jan 2023.

Builds on

16 of 21 references.

References · 16

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
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  3. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
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  10. 2020Data-Free Network Quantization With Adversarial Knowledge Distillationcited by 80
  11. 2020Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectorscited by 80
  12. 2020μNAS: Constrained Neural Architecture Search for Microcontrollerscited by 55
4 more
  1. 2020Mix and Match: A Novel FPGA-Centric Deep Neural Network Quantization Frameworkcited by 27
  2. 2021BatchQuant: Quantized-for-all Architecture Search with Robust Quantizercited by 20
  3. 2021Generative Zero-shot Network Quantizationcited by 15
  4. 2018Joint Neural Architecture Search and Quantizationcited by 13

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 1; 2026: 1.

4 papers.

  1. 2023Sensitivity-Aware Mixed-Precision Quantization and Width Optimization of Deep Neural Networks Through Cluster-Based Tree-Structured Parzen Estimationcited by 3
  2. 2025MiCo: End-to-End Mixed Precision Neural Network Co-Exploration Framework for Edge AIcited by 2
  3. 2024Scaling Up Quantization-Aware Neural Architecture Search for Efficient Deep Learning on the Edgecited by 0
  4. 2026MiCoPro: End-to-End Mixed Precision HW/SW Co-design with HW-aware Proxy Modelcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023SimQ-NAS: Simultaneous Quantization Policy and Neural Architecture Search
  2. 2021BMPQ: Bit-Gradient Sensitivity Driven Mixed-Precision Quantization of DNNs from Scratch
  3. 2023Free Bits: Latency Optimization of Mixed-Precision Quantized Neural Networks on the Edge
  4. 2023Small Temperature is All You Need for Differentiable Architecture Search
  5. 2022Channel-wise Mixed-precision Assignment for DNN Inference on Constrained Edge Nodes
  6. 2021Towards Mixed-Precision Quantization of Neural Networks via Constrained Optimization
  7. 2026Task-Aware QUBO Allocation for Mixed-Precision Quantization
  8. 2023A Practical Mixed Precision Algorithm for Post-Training Quantization