# Silhouette: Toward Performance-Conscious and Transferable CPU Embeddings

arXiv: https://arxiv.org/abs/2212.08046 · HTML: https://mlsanity.com/paper/2212.08046/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 11 references that are in the map.
- First posted on arXiv 15 Dec 2022.

## Builds on (most cited first, 2 of 2)

- [What makes ImageNet good for transfer learning?](https://mlsanity.com/paper/1608.08614/) (2016)
- [A Tutorial on Network Embeddings](https://mlsanity.com/paper/1808.02590/) (2018)

## Built on by (most cited first, 1 of 1)

- [GraphPerf-RT: A Graph-Driven Performance Model for Hardware-Aware Scheduling of OpenMP Codes](https://mlsanity.com/paper/2512.12091/) (2025)

## Abstract (arXiv)

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.