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 stuli@princeton.edu.
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.