# BOMP-NAS: Bayesian Optimization Mixed Precision NAS

arXiv: https://arxiv.org/abs/2301.11810 · HTML: https://mlsanity.com/paper/2301.11810/

## 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 (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Once-for-All: Train One Network and Specialize it for Efficient Deployment](https://mlsanity.com/paper/1908.09791/) (2019)
- [A White Paper on Neural Network Quantization](https://mlsanity.com/paper/2106.08295/) (2021)
- [Auto-Keras: An Efficient Neural Architecture Search System](https://mlsanity.com/paper/1806.10282/) (2018)
- [Efficient Multi-objective Neural Architecture Search via Lamarckian Evolution](https://mlsanity.com/paper/1804.09081/) (2018)
- [Mixed Precision Quantization of ConvNets via Differentiable Neural Architecture Search](https://mlsanity.com/paper/1812.00090/) (2018)
- [APQ: Joint Search for Network Architecture, Pruning and Quantization Policy](https://mlsanity.com/paper/2006.08509/) (2020)
- [Data-Free Network Quantization With Adversarial Knowledge Distillation](https://mlsanity.com/paper/2005.04136/) (2020)
- [Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors](https://mlsanity.com/paper/2006.10159/) (2020)
- [μNAS: Constrained Neural Architecture Search for Microcontrollers](https://mlsanity.com/paper/2010.14246/) (2020)
- [Mix and Match: A Novel FPGA-Centric Deep Neural Network Quantization Framework](https://mlsanity.com/paper/2012.04240/) (2020)
- [BatchQuant: Quantized-for-all Architecture Search with Robust Quantizer](https://mlsanity.com/paper/2105.08952/) (2021)
- [Generative Zero-shot Network Quantization](https://mlsanity.com/paper/2101.08430/) (2021)
- [Joint Neural Architecture Search and Quantization](https://mlsanity.com/paper/1811.09426/) (2018)

## Built on by (most cited first, 4 of 4)

- [Sensitivity-Aware Mixed-Precision Quantization and Width Optimization of Deep Neural Networks Through Cluster-Based Tree-Structured Parzen Estimation](https://mlsanity.com/paper/2308.06422/) (2023)
- [MiCo: End-to-End Mixed Precision Neural Network Co-Exploration Framework for Edge AI](https://mlsanity.com/paper/2508.09500/) (2025)
- [Scaling Up Quantization-Aware Neural Architecture Search for Efficient Deep Learning on the Edge](https://mlsanity.com/paper/2401.12350/) (2024)
- [MiCoPro: End-to-End Mixed Precision HW/SW Co-design with HW-aware Proxy Model](https://mlsanity.com/paper/2608.06916/) (2026)

## 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.