Abstract: A system and method for detecting Trojans and other intermittent severe defects in a digital circuit design. A simulation of the digital circuit design results in a value change dump file, which is compiled to form a value change summary file containing counts of the numbers of value changes for the signals in the digital circuit design. A discriminative neural network analyzes the value change summary file to determine whether an intermittent severe defect is present. A corpus of digital circuit designs, with and without intermittent severe defects, is used to train the discriminative neural network. The training process may involve dimensionality reduction of the data, enlargement of the data set, and data compression using an autoencoder.

Abstract: A system and method for detecting Trojans and other intermittent severe defects in a digital circuit design. A simulation of the digital circuit design results in a value change dump file, which is compiled to form a value change summary file containing counts of the numbers of value changes for the signals in the digital circuit design. A discriminative neural network analyzes the value change summary file to determine whether an intermittent severe defect is present. A corpus of digital circuit designs, with and without intermittent severe defects, is used to train the discriminative neural network. The training process may involve dimensionality reduction of the data, enlargement of the data set, and data compression using an autoencoder.