On Functional Test Generation for Deep Neural Network IPs
This addresses the need for DNN IP vendors to validate functionality securely for users, though it is incremental as it builds on existing gradient-based methods.
The paper tackles the problem of generating functional test cases for deep neural network intellectual property (IP) cores without leaking parameters, by selecting samples from the training set and using a gradient-based method to create new tests that activate parameters and propagate perturbations to outputs, with experimental results showing efficacy.
Machine learning systems based on deep neural networks (DNNs) produce state-of-the-art results in many applications. Considering the large amount of training data and know-how required to generate the network, it is more practical to use third-party DNN intellectual property (IP) cores for many designs. No doubt to say, it is essential for DNN IP vendors to provide test cases for functional validation without leaking their parameters to IP users. To satisfy this requirement, we propose to effectively generate test cases that activate parameters as many as possible and propagate their perturbations to outputs. Then the functionality of DNN IPs can be validated by only checking their outputs. However, it is difficult considering large numbers of parameters and highly non-linearity of DNNs. In this paper, we tackle this problem by judiciously selecting samples from the DNN training set and applying a gradient-based method to generate new test cases. Experimental results demonstrate the efficacy of our proposed solution.