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Fathom: Reference Workloads for Modern Deep Learning

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Release: 1.0-rc0

This release reflects the state of Fathom more or less as it was for the paper published in September 2016. We are currently developing a somewhat more user-friendly version, which you can track in the GitHub issue tracker. If you're eager to use Fathom as it is, please let us know.


This paper contains a description of the workloads, performance characteristics, and the rationale behind the project:

R. Adolf, S. Rama, B. Reagen, G.Y. Wei, D. Brooks. "Fathom: Reference Workloads for Modern Deep Learning Methods." (Arxiv) (DOI)

Name Description
Seq2Seq Direct language-to-language sentence translation. State-of-the-art accuracy with a simple, language-agnostic architecture.
MemNet Facebook's memory-oriented neural system. One of two novel architectures which explore a topology beyond feed-forward lattices of neurons.
Speech Baidu's speech recognition engine. Proved purely deep-learned networks can beat hand-tuned systems.
Autoenc Variational autoencoder. An efficient, generative model for feature learning.
Residual Image classifier from Microsoft Research Asia. Dramatically increased the practical depth of convolutional networks. ILSVRC 2015 winner.
VGG Image classifier demonstrating the power of small convolutional filters. ILSVRC 2014 winner.
AlexNet Image classifier. Watershed for deep learning by beating hand-tuned image systems at ILSVRC 2012.
DeepQ Atari-playing neural network from DeepMind. Achieves superhuman performance on majority of Atari2600 games, without any preconceptions.

Getting Started

Read the Fathom Quickstart Guide and let us know if you have any questions.

Submit a GitHub issue if you have a suggestion or find a bug.


Reference workloads for modern deep learning methods.







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