# Mixed-Precision Quantization: Make the Best Use of Bits Where They Matter Most

arXiv: https://arxiv.org/abs/2412.03101 · HTML: https://mlsanity.com/paper/2412.03101/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 57 references that are in the map.
- First posted on arXiv 4 Dec 2024.

## Builds on (most cited first, 11 of 11)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Networks](https://mlsanity.com/paper/1712.01507/) (2017)
- [Towards Mixed-Precision Quantization of Neural Networks via Constrained Optimization](https://mlsanity.com/paper/2110.06554/) (2021)
- [A Survey on Large-scale Machine Learning](https://mlsanity.com/paper/2008.03911/) (2020)
- [A Tutorial on Extremely Large-Scale MIMO for 6G: Fundamentals, Signal Processing, and Applications](https://mlsanity.com/paper/2307.07340/) (2023)
- [Differentially Quantized Gradient Methods](https://mlsanity.com/paper/2002.02508/) (2020)
- [Subspace Learning From Bits](https://mlsanity.com/paper/1407.6288/) (2014)
- [Resource Allocation and Dithering of Bayesian Parameter Estimation Using Mixed-Resolution Data](https://mlsanity.com/paper/2009.08081/) (2020)
- [Rigorous Roundoff Error Analysis of Probabilistic Floating-Point Computations](https://mlsanity.com/paper/2105.13217/) (2021)
- [CRB Analysis for Mixed-ADC Based DOA Estimation](https://mlsanity.com/paper/2403.09301/) (2024)
- [Meta-Heuristic Fronthaul Bit Allocation for Cell-free Massive MIMO Systems](https://mlsanity.com/paper/2403.19132/) (2024)

## Built on by (most cited first, 2 of 2)

- [Mixed-Precision Quantization for Language Models: Techniques and Prospects](https://mlsanity.com/paper/2510.16805/) (2025)
- [Scale When Needed: Adaptive Neuron-level Mixed Precision Quantization Aware Training](https://mlsanity.com/paper/2605.25054/) (2026)

## Abstract (arXiv)

Mixed-precision quantization offers superior performance to fixed-precision quantization. It has been widely used in signal processing, communication systems, and machine learning. In mixed-precision quantization, bit allocation is essential. Hence, in this paper, we propose a new bit allocation framework for mixed-precision quantization from a search perspective. First, we formulate a general bit allocation problem for mixed-precision quantization. Then we introduce the penalized particle swarm optimization (PPSO) algorithm to address the integer consumption constraint. To improve efficiency and avoid iterations on infeasible solutions within the PPSO algorithm, a greedy criterion particle swarm optimization (GC-PSO) algorithm is proposed. The corresponding convergence analysis is derived based on dynamical system theory. Furthermore, we apply the above framework to some specific classic fields, i.e., finite impulse response (FIR) filters, receivers, and gradient descent. Numerical examples in each application underscore the superiority of the proposed framework to the existing algorithms.