GoldenBox

Overview • Installing • Code • Acknowledgements • License

Overview

GoldenEye Object Detection is an extension of the original GoldenEye functional simulator with fault injection capabilities for common and emerging numerical formats. Previously the simulator was only supported image classification models. Now, we have extended our use case to object detection models as well.

Installing

Ubuntu-20.04 or later

1. Clone the goldeneye repository.

git clone https://github.com/sajidahmed12/goldeneye-object-detection

2. Download ninja-build, which is needed for qtorch.

sudo apt install ninja-build

3. Install the other project dependencies from the requirements.txt file.

pip install -r requirements.txt

Testing

pytest val/test_num_sys.py

Code

Structure

The scripts folder includes wrappers around the goldeneye-obj framework to simplify its use. The src folder contains all of the core components, such as number system implementation, error injection routines, dataloaders, etc. The val folder is used for unit testing the code. You can run it using pytest to check that the installation process was successful.

Example Outputs are saved in this Google Drive link

Example Commands

Pre-processing

python preprocess.py -b 16 -n frcnn -d COCO -w 8 -P FP32 -f fp_n  -C 0 [MS-COCO FP32](MS-COCO FP32])

Profiling

python profiling.py -b 16 -n frcnn -d COCO -w 16 -P FP32 -f fp_n -B 32 -R 23

Split Data

python split_data.py -b 16 -n frcnn -d COCO -o -w 16 -P FP32 -f fp_n -B 32 -R 23

Error Injections

python injections.py -b 16 -n frcnn -d COCO -w 16 -P FP32 -i 102400 -I 1 -f fp_n -B 32 -R 23

Post-processing

python postprocess.py -b 16 -n frcnn -d COCO -w 16 -P FP32 -i 102400 -I 1 -f fp_n -B 32 -R 23

Acknowledgements

• This Repository was forked from Goldeneye developed and maintained by Sajid Ahmed & Dr. Abdulrahman Mahmoud

License

MIT License