# EMQ: Evolving Training-free Proxies for Automated Mixed Precision Quantization

arXiv: https://arxiv.org/abs/2307.10554 · HTML: https://mlsanity.com/paper/2307.10554/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 46 of its 60 references that are in the map.
- First posted on arXiv 20 Jul 2023.

## Builds on (most cited first, 30 of 46)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [DARTS: Differentiable Architecture Search](https://mlsanity.com/paper/1806.09055/) (2018)
- [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020)
- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [SNIP: Single-shot Network Pruning based on Connection Sensitivity](https://mlsanity.com/paper/1810.02340/) (2018)
- [Learned Step Size Quantization](https://mlsanity.com/paper/1902.08153/) (2019)
- [Quantizing deep convolutional networks for efficient inference: A whitepaper](https://mlsanity.com/paper/1806.08342/) (2018)
- [Pruning neural networks without any data by iteratively conserving synaptic flow](https://mlsanity.com/paper/2006.05467/) (2020)
- [BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction](https://mlsanity.com/paper/2102.05426/) (2021)
- [Picking Winning Tickets Before Training by Preserving Gradient Flow](https://mlsanity.com/paper/2002.07376/) (2020)
- [PACT: Parameterized Clipping Activation for Quantized Neural Networks](https://mlsanity.com/paper/1805.06085/) (2018)
- [HAQ: Hardware-Aware Automated Quantization with Mixed Precision](https://mlsanity.com/paper/1811.08886/) (2018)
- [Single Path One-Shot Neural Architecture Search with Uniform Sampling](https://mlsanity.com/paper/1904.00420/) (2019)
- [HAWQ: Hessian AWare Quantization of Neural Networks with Mixed-Precision](https://mlsanity.com/paper/1905.03696/) (2019)
- [LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks](https://mlsanity.com/paper/1807.10029/) (2018)
- [ZeroQ: A Novel Zero Shot Quantization Framework](https://mlsanity.com/paper/2001.00281/) (2020)
- [HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks](https://mlsanity.com/paper/1911.03852/) (2019)
- [Neural Architecture Search without Training](https://mlsanity.com/paper/2006.04647/) (2020)
- [Progressive Differentiable Architecture Search: Bridging the Depth Gap between Search and Evaluation](https://mlsanity.com/paper/1904.12760/) (2019)
- [AutoML-Zero: Evolving Machine Learning Algorithms From Scratch](https://mlsanity.com/paper/2003.03384/) (2020)
- [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)
- [Faster gaze prediction with dense networks and Fisher pruning](https://mlsanity.com/paper/1801.05787/) (2018)
- [Rethinking Differentiable Search for Mixed-Precision Neural Networks](https://mlsanity.com/paper/2004.05795/) (2020)
- [Zen-NAS: A Zero-Shot NAS for High-Performance Deep Image Recognition](https://mlsanity.com/paper/2102.01063/) (2021)
- [AdaBits: Neural Network Quantization with Adaptive Bit-Widths](https://mlsanity.com/paper/1912.09666/) (2019)
- [AutoQ: Automated Kernel-Wise Neural Network Quantization](https://mlsanity.com/paper/1902.05690/) (2019)
- [HAWQV3: Dyadic Neural Network Quantization](https://mlsanity.com/paper/2011.10680/) (2020)

## Built on by (most cited first, 22 of 22)

- [Pruner-Zero: Evolving Symbolic Pruning Metric from scratch for Large Language Models](https://mlsanity.com/paper/2406.02924/) (2024)
- [STBLLM: Breaking the 1-Bit Barrier with Structured Binary LLMs](https://mlsanity.com/paper/2408.01803/) (2024)
- [CLAMP-ViT: Contrastive Data-Free Learning for Adaptive Post-Training Quantization of ViTs](https://mlsanity.com/paper/2407.05266/) (2024)
- [Auto-Prox: Training-Free Vision Transformer Architecture Search via Automatic Proxy Discovery](https://mlsanity.com/paper/2312.09059/) (2023)
- [NoRA: Nested Low-Rank Adaptation for Efficient Fine-Tuning Large Models](https://mlsanity.com/paper/2408.10280/) (2024)
- [Algorithm-Hardware Co-Design of Distribution-Aware Logarithmic-Posit Encodings for Efficient DNN Inference](https://mlsanity.com/paper/2403.05465/) (2024)
- [LPZero: Language Model Zero-cost Proxy Search from Zero](https://mlsanity.com/paper/2410.04808/) (2024)
- [Pruning Large Language Models via Accuracy Predictor](https://mlsanity.com/paper/2309.09507/) (2023)
- [ParZC: Parametric Zero-Cost Proxies for Efficient NAS](https://mlsanity.com/paper/2402.02105/) (2024)
- [Bandwidth-Aware and Overlap-Weighted Compression for Communication-Efficient Federated Learning](https://mlsanity.com/paper/2408.14736/) (2024)
- [InfoQ: Mixed-Precision Quantization via Global Information Flow](https://mlsanity.com/paper/2508.04753/) (2025)
- [TVT: Training-Free Vision Transformer Search on Tiny Datasets](https://mlsanity.com/paper/2311.14337/) (2023)
- [MixA-Q: Revisiting Activation Sparsity for Vision Transformers from a Mixed-Precision Quantization Perspective](https://mlsanity.com/paper/2507.19131/) (2025)
- [Enhancing Large Multimodal Models with Adaptive Sparsity and KV Cache Compression](https://mlsanity.com/paper/2507.20613/) (2025)
- [On-Chip Hardware-Aware Quantization for Mixed Precision Neural Networks](https://mlsanity.com/paper/2309.01945/) (2023)
- [W-PCA Based Gradient-Free Proxy for Efficient Search of Lightweight Language Models](https://mlsanity.com/paper/2504.15983/) (2025)
- [Structure-Aware Automatic Channel Pruning by Searching with Graph Embedding](https://mlsanity.com/paper/2506.11469/) (2025)
- [Where and How to Enhance: Discovering Bit-Width Contribution for Mixed Precision Quantization](https://mlsanity.com/paper/2508.03002/) (2025)
- [SONIQ: System-Optimized Noise-Injected Ultra-Low-Precision Quantization with Full-Precision Parity](https://mlsanity.com/paper/2311.14114/) (2023)
- [Towards Training-free Automatic Proxy Discovery via Large Language Models for Mixed Precision Quantization](https://mlsanity.com/paper/2512.07419/) (2025)
- [Scale When Needed: Adaptive Neuron-level Mixed Precision Quantization Aware Training](https://mlsanity.com/paper/2605.25054/) (2026)
- [RAMP: Robust Adaptive Mixed-Precision Quantization for Edge CPU Vision Models](https://mlsanity.com/paper/2609.28262/) (2026)

## Abstract (arXiv)

Mixed-Precision Quantization~(MQ) can achieve a competitive accuracy-complexity trade-off for models. Conventional training-based search methods require time-consuming candidate training to search optimized per-layer bit-width configurations in MQ. Recently, some training-free approaches have presented various MQ proxies and significantly improve search efficiency. However, the correlation between these proxies and quantization accuracy is poorly understood. To address the gap, we first build the MQ-Bench-101, which involves different bit configurations and quantization results. Then, we observe that the existing training-free proxies perform weak correlations on the MQ-Bench-101. To efficiently seek superior proxies, we develop an automatic search of proxies framework for MQ via evolving algorithms. In particular, we devise an elaborate search space involving the existing proxies and perform an evolution search to discover the best correlated MQ proxy. We proposed a diversity-prompting selection strategy and compatibility screening protocol to avoid premature convergence and improve search efficiency. In this way, our Evolving proxies for Mixed-precision Quantization~(EMQ) framework allows the auto-generation of proxies without heavy tuning and expert knowledge. Extensive experiments on ImageNet with various ResNet and MobileNet families demonstrate that our EMQ obtains superior performance than state-of-the-art mixed-precision methods at a significantly reduced cost. The code will be released.