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[TMC'23] EdgeTran: Device-Aware Co-Search of Transformers and Mobile Platforms

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EdgeTran: Device-Aware Co-Search of Transformers for Efficient Inference on Mobile Edge Platforms

Python Version Conda PyTorch

This repository constains the source code for the published IEEE Transactions on Mobile Computing paper. EdgeTran evaluates different transformer architectures on a diverse set of embedded platforms for various natural language processing tasks. This repository uses the FlexiBERT framework (jha-lab/txf_design-space) to obtain the design space of flexible and heterogeneous transformer models.

Supported platforms:

  • Linux on x86 CPUs with CUDA GPUs (tested on AMD EPYC Rome CPU, Intel Core i7-8650U CPU and Nvidia A100 GPU).
  • Apple M1 and M1-Pro SoC on iPad and MacBook Pro respectively.
  • Broadcom BCM2711 SoC on Raspberry Pi 4 Model-B.
  • Intel Neural Compute Stick v2.
  • Nvidia Tegra X1 SoC on Nvidia Jetson Nano 2GB.

Developer

Shikhar Tuli. For any questions, comments or suggestions, please reach me at [email protected].

Cite this work

Cite our work using the following bitex entry:

@article{tuli2023edgetran,
  title={{EdgeTran}: Device-Aware Co-Search of Transformers for Efficient Inference on Mobile Edge Platforms},
  author={Tuli, Shikhar and Jha, Niraj K},
  journal={IEEE Transactions on Mobile Computing},
  year={2023}
}

License

BSD-3-Clause. Copyright (c) 2023, Shikhar Tuli and Jha Lab. All rights reserved.

See License file for more details.

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[TMC'23] EdgeTran: Device-Aware Co-Search of Transformers and Mobile Platforms

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