Silhouette: Toward Performance-Conscious and Transferable CPU Embeddings

2022/12/15 by Tarikul Islam Papon, Papon, Tarikul Islam, Abdul Wasay +1 · 1 citation
Computer Science · Neuroscience · #Context-Aware Activity Recognition Systems #EEG and Brain-Computer Interfaces #FOS: Computer and information sciences #Machine Learning (cs.LG) #Software System Performance and Reliability

paper · pdf · doi:10.48550/arxiv.2212.08046

openalex publication_date 2022/12/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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 Silhouette, our approach that leverages publicly-available performance data sets to learn CPU embeddings. We show how these embeddings enable transfer learning between data sets of different types and sizes. Each of these scenarios leads to an improvement in accuracy for the target data set.

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