# Swift for TensorFlow: A portable, flexible platform for deep learning

arXiv: https://arxiv.org/abs/2102.13243 · HTML: https://mlsanity.com/paper/2102.13243/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 48 references that are in the map.
- First posted on arXiv 26 Feb 2021.

## Builds on (most cited first, 17 of 17)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [In-Datacenter Performance Analysis of a Tensor Processing Unit](https://mlsanity.com/paper/1704.04760/) (2017)
- [OpenSpiel: A Framework for Reinforcement Learning in Games](https://mlsanity.com/paper/1908.09453/) (2019)
- [MLPerf Training Benchmark](https://mlsanity.com/paper/1910.01500/) (2019)
- [Glow: Graph Lowering Compiler Techniques for Neural Networks](https://mlsanity.com/paper/1805.00907/) (2018)
- [Instead of Rewriting Foreign Code for Machine Learning, Automatically Synthesize Fast Gradients](https://mlsanity.com/paper/2010.01709/) (2020)
- [TensorFlow Eager: A Multi-Stage, Python-Embedded DSL for Machine Learning](https://mlsanity.com/paper/1903.01855/) (2019)
- [Chainer: A Deep Learning Framework for Accelerating the Research Cycle](https://mlsanity.com/paper/1908.00213/) (2019)
- [DyNet: The Dynamic Neural Network Toolkit](https://mlsanity.com/paper/1701.03980/) (2017)
- [Jelly Bean World: A Testbed for Never-Ending Learning](https://mlsanity.com/paper/2002.06306/) (2020)
- [Demystifying Differentiable Programming: Shift/Reset the Penultimate Backpropagator](https://mlsanity.com/paper/1803.10228/) (2018)
- [Automatic Full Compilation of Julia Programs and ML Models to Cloud TPUs](https://mlsanity.com/paper/1810.09868/) (2018)
- [Automatic Differentiation in ROOT](https://mlsanity.com/paper/2004.04435/) (2020)

## Built on by (most cited first, 6 of 6)

- [Equinox: neural networks in JAX via callable PyTrees and filtered transformations](https://mlsanity.com/paper/2111.00254/) (2021)
- [Torch.fx: Practical Program Capture and Transformation for Deep Learning in Python](https://mlsanity.com/paper/2112.08429/) (2021)
- [An automatic differentiation system for the age of differential privacy](https://mlsanity.com/paper/2109.10573/) (2021)
- [Machines of finite depth: towards a formalization of neural networks](https://mlsanity.com/paper/2204.12786/) (2022)
- [Probabilistic Tracking with Deep Factors](https://mlsanity.com/paper/2112.01609/) (2021)
- [Differentiate the Evaluator, Not the Program: An Efficient Runtime Representation for Neuro-Symbolic Learning](https://mlsanity.com/paper/2607.03574/) (2026)

## Abstract (arXiv)

Swift for TensorFlow is a deep learning platform that scales from mobile devices to clusters of hardware accelerators in data centers. It combines a language-integrated automatic differentiation system and multiple Tensor implementations within a modern ahead-of-time compiled language oriented around mutable value semantics. The resulting platform has been validated through use in over 30 deep learning models and has been employed across data center and mobile applications.