Silhouette: Toward Performance-Conscious and Transferable CPU Embeddings
Tarikul Islam Papon ⋅ Abdul Wasay
Abstract
Learned embeddings are widely used to obtain concise data representation and enable transfer learning between different data sets and tasks. In this paper, we present our approach Silhouette, that leverages publicly-available CPU performance data sets to learn CPU performance embeddings. We show how Silhouette enables transfer learning across different types of CPU and leads to a significant improvement in performance prediction accuracy for the target CPUs.
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