# MetaDSE: A Few-shot Meta-learning Framework for Cross-workload CPU Design Space Exploration

arXiv: https://arxiv.org/abs/2504.13568 · HTML: https://mlsanity.com/paper/2504.13568/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 31 references that are in the map.
- First posted on arXiv 18 Apr 2025.

## Builds on (most cited first, 7 of 7)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Meta-Learning for Semi-Supervised Few-Shot Classification](https://mlsanity.com/paper/1803.00676/) (2018)
- [Meta-Transfer Learning for Few-Shot Learning](https://mlsanity.com/paper/1812.02391/) (2018)
- [A Closer Look at Few-shot Classification Again](https://mlsanity.com/paper/2301.12246/) (2023)
- [Learning Pareto-Frontier Resource Management Policies for Heterogeneous SoCs: An Information-Theoretic Approach](https://mlsanity.com/paper/2105.09282/) (2021)
- [Multi-objective Optimization in CPU Design Space Exploration: Attention is All You Need](https://mlsanity.com/paper/2410.18368/) (2024)
- [Explainable Fuzzy Neural Network with Multi-Fidelity Reinforcement Learning for Micro-Architecture Design Space Exploration](https://mlsanity.com/paper/2412.10754/) (2024)

## Abstract (arXiv)

Cross-workload design space exploration (DSE) is crucial in CPU architecture design. Existing DSE methods typically employ the transfer learning technique to leverage knowledge from source workloads, aiming to minimize the requirement of target workload simulation. However, these methods struggle with overfitting, data ambiguity, and workload dissimilarity. To address these challenges, we reframe the cross-workload CPU DSE task as a few-shot meta-learning problem and further introduce MetaDSE. By leveraging model agnostic meta-learning, MetaDSE swiftly adapts to new target workloads, greatly enhancing the efficiency of cross-workload CPU DSE. Additionally, MetaDSE introduces a novel knowledge transfer method called the workload-adaptive architectural mask algorithm, which uncovers the inherent properties of the architecture. Experiments on SPEC CPU 2017 demonstrate that MetaDSE significantly reduces prediction error by 44.3% compared to the state-of-the-art. MetaDSE is open-sourced and available at this hrefhttps://anonymous.4open.science/r/MetaDSE-02F8anonymous GitHub.