EdgeBERT is a HW/SW co-design enabling sentence-level energy optimizations for latency-aware multi-task NLP inference. In this repo, we provide both the software and hardware modelings. For full details, please check out this paper.
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hw/cmod/include/*.h
contains header files for functions and classes used in the EdgeBERT hardware accelerator -
hw/cmod/<module>
sub-directories contain EdgeBERT HW SystemC modules -
hw/matchlib
library of synthesizable hardware components from NVIDIA -
sw/Entropy_LUT
contains the entropy datasets and a notebook to train the entropy prediction LUT (as well as a sample pretrained LUT) -
sw/EdgeBERT/transformers
contains a modified version of the HuggingFace Transformers library -
sw/EdgeBERT/examples
contains python scripts for training and evaluating models -
sw/EdgeBERT/scripts
contains shell scripts to run our software workflow
If you find this resource useful, please consider citing the following paper:
@inproceedings{edgebert_micro2021,
author = {Tambe, Thierry and Hooper, Coleman and Pentecost, Lillian and Jia, Tianyu
and Yang, En-Yu and Donato, Marco and Sanh, Victor and Whatmough, Paul N.
and Rush, Alexander M. and Brooks, David and Wei, Gu-Yeon},
title = {EdgeBERT: Sentence-Level Energy Optimizations for Latency-Aware Multi-Task NLP Inference},
publisher = {Association for Computing Machinery},
booktitle = {Proceedings of the 54th Annual IEEE/ACM International Symposium on Microarchitecture},
year = {2021},
}
For any further questions please contact [email protected]