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.

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

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}
}

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

See License file for more details.