Fault injection on LeNet

A coarse-grained configuation fault inject experiment

For example, the following code perform a quantized random integer bit flip injection on LeNet.

Setup LeNet default fault injection

from dataset.lenet_cifar import make_testloader, LeNet
from mrfi import MRFI, EasyConfig
from mrfi.experiment import Acc_experiment, Acc_golden, BER_Acc_experiment

testloader = make_testloader(1000, batch_size = 128) # test on 1000 cifar-10 images

# Create fault inject model
fi_model = MRFI(LeNet(trained=True).eval(), 
                EasyConfig.load_preset('default_fi'))

# fi_model can be used as regular PyTorch model
print(fi_model(torch.zeros(1,3,32,32)).shape)

Simple fault injection acccuracy experiment

# Test accuracy under fault injection with select rate = 1e-3,
#  which specified in "easyconfigs/default_fi"
print('FI Acc: ', Acc_experiment(fi_model, dataloader))

# Test accuracy w/o fault inject
with fi_model.golden_run():
    print('golden run Acc: ', Acc_experiment(fi_model, dataloader))
# Another way to get golden run accuracy 
print('golden run Acc: ', Acc_golden(fi_model, dataloader))

Find the relation between bit error rate (BER) and classification accuracy.

BER_Acc_experiment

# Get selector handler because BER_Acc_experiment needs to modify selection rate in experiment
selector_cfg = fi_model.get_activation_configs('selector')
BER, Acc = BER_Acc_experiment(fi_model, selector_cfg, 
                              make_testloader(1000, batch_size = 128), 
                              [1e-6, 1e-5, 1e-4, 1e-3])