Datasets
Standard Dataset
Convolutional neural network errors
- Citation Author(s):
- Fernando Fernandes dos Santos
- Submitted by:
- Fernando dos Santos
- Last updated:
- Tue, 05/17/2022 - 22:17
- DOI:
- 10.21227/H2WT0P
- Data Format:
- Research Article Link:
- License:
- Categories:
- Keywords:
Abstract
This file contains all data used on paper "Analyzing and Increasing the Reliability of Convolutional Neural Networks on GPUs"
Data is organized in the following files:
-- sassifi_inst: contains all errors obtained on fault injections using INST mode
-- sassifi_rf: contains all errors obtained on fault injections using RF mode
-- ecc_on: contains all observed errors under the beam for K40 with ECC enabled
-- ecc_off: contains all observed errors under the beam for K40 (ECC OFF), Titan X, and X
Each file is organized as follows:
BENCHMARK_AND_MACHINE_NAME: which could be cudaDarknet_carol-k402 for YOLO(Darknet) on K40, PyFasterRcnn_carol-k402 Faster RCNN on K40, cudaDarknet_carolx1 Darknet on Tegra X1, PyFasterRcnn_carol-tx Faster RCNN on Titan X or cudaDarknet_carol-tx Darknet on Titan X
log_name:\
Each log output file which contains:
-date and time of the test
sdc_iteration
-iteration of SDC, once not all executions produced SDC
-
it_errors: how many errors in this SDC
-
ERROR_LIST
All errors listed, to compare with golden value the errors are printed always using the value read (x_r, y_r, prob_r for object probability, h_r for height, w_r for width) and expected value (x_e, y_e, prob_e for object probability, h_e for height, w_e for width)