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

--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. T o address these challenges, we reframe the cross-workload CPU DSE task as a few-shot meta-learning problem and further introduce MetaDSE. 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 anonymous GitHub. Design Space Exploration (DSE) is pivotal in modern CPU architecture design, enabling the optimization of configurations to balance multiple objectives like performance, power, and area (PP A) [1], [2]. The increasing complexity of CPU architectures has led to an exponential expansion of the design space, presenting significant challenges in efficiently identifying optimal configurations.