A Hybrid Sparse-dense Defensive DNN Accelerator Architecture against Adversarial Example Attacks

Abstract

Understanding how to defend against adversarial attacks is crucial for ensuring the safety and reliability of these systems in real-world applications. Various adversarial defense methods are proposed, which aim to improve the robustness of neural networks against adversarial attacks by changing the model structure, adding detection networks, and adversarial purification network. However, deploying adversarial defense methods in existing DNN accelerators or defensive accelerators leads to many key issues. To address these challenges, this paper proposes sDNNGuard , an elastic heterogeneous DNN accelerator architecture that can efficiently orchestrate the simultaneous execution of original ( target ) DNN networks and the detect algorithm or network. It not only supports for dense DNN detect algorithms, but also allows for sparse DNN defense methods and other mixed dense-sparse (e.g., dense-dense and sparse-dense) workloads to fully exploit the benefits of sparsity. sDNNGuard with a CPU core also supports the non-DNN computing and allows the special layer of the neural network, and used for the conversion for sparse storage format for weights and activation values. To reduce off-chip traffic and improve resources utilization, a new hardware abstraction with elastic on-chip buffer/computing resource management is proposed to achieve dynamical resource scheduling mechanism. We propose an extended AI instruction set for neural networks synchronization, task scheduling and efficient data interaction. Experiment results show that sDNNGuard can effectively validate the legitimacy of the input samples in parallel with the target DNN model, achieving an average 1.42 × speedup compared with the state-of-the-art accelerators.