Several safety-critical applications such as self-navigation, health care, and industrial control systems use embedded systems as their core. Recent advancements in Neural Networks (NNs) in approximating complex functions make them well-suited for these domains. However, the compute-intensive nature of NNs limits their deployment and training in embedded systems with limited computation and storage capacities. Moreover, the adversarial vulnerability of NNs challenges their use in safety-critical scenarios. Hence, developing sparse models having robustness guarantees while leveraging fewer resources during training is critical in expanding NNs' use in safety-critical and resource-constrained embedding system settings. This paper presents 'VeriSparse'-- a framework to search verified locally robust sparse networks starting from a random sparse initialization (i.e., scratch). VeriSparse obtains sparse NNs exhibiting similar or higher verified local robustness, requiring one-third of the training time compared to the state-of-the-art approaches. Furthermore, VeriSparse performs both structured and unstructured sparsification, enabling storage, computing-resource, and computation time reduction during inference generation. Thus, it facilitates the resource-constraint embedding platforms to leverage verified robust NN models, expanding their scope to safety-critical, real-time, and edge applications. We exhaustively investigated VeriSparse's efficacy and generalizability by evaluating various benchmark and application-specific datasets across several model architectures.

翻译：多项安全关键应用（如自主导航、医疗保健和工业控制系统）都以嵌入式系统为核心。神经网络在逼近复杂函数方面的最新进展使其非常适合这些领域。然而，神经网络计算密集的特性限制了其在计算和存储能力有限的嵌入式系统中的部署和训练。此外，神经网络的对抗性脆弱性对其在安全关键场景中的应用构成挑战。因此，在训练过程中利用更少资源的同时开发具有鲁棒性保证的稀疏模型，对于扩展神经网络在安全关键和资源受限的嵌入式系统环境中的应用至关重要。本文提出"VeriSparse"——一个从随机稀疏初始化（即从零开始）搜索可验证局部鲁棒稀疏网络的框架。VeriSparse 获得的稀疏神经网络展现出相似或更高的可验证局部鲁棒性，且训练时间仅为现有最先进方法的三分之一。此外，VeriSparse 同时执行结构化与非结构化稀疏化，在推理生成阶段实现存储、计算资源和计算时间的缩减。因此，它使资源受限的嵌入式平台能够利用可验证鲁棒神经网络模型，将其应用范围扩展至安全关键、实时和边缘应用。我们通过在多种模型架构上评估基准数据集和特定应用数据集，全面探究了 VeriSparse 的有效性与泛化能力。