论文标题
VericSouppress：一种简化已验证的可靠压缩神经网络合成的工具
VeriCompress: A Tool to Streamline the Synthesis of Verified Robust Compressed Neural Networks from Scratch
论文作者
论文摘要
AI的广泛集成导致了边缘的神经网络（NNS）部署，并且针对安全至关重要的情况进行了类似的限量资源平台。然而，NN的脆弱性引起了人们对可靠推理的担忧。此外，受限的平台需要紧凑的网络。这项研究介绍了Vericompress，该工具可自动使用稳健性的压缩模型进行搜索和培训。这些模型非常适合安全至关重要的应用，并遵守预定义的体系结构和大小限制，使其可在资源限制的平台上部署。该方法训练模型比最先进的方法快2-3倍，以平均准确性和鲁棒性提高15.1和9.8个百分点，超过了相关的基线方法。当部署在资源限制的通用平台上时，这些模型所需的内存少5-8倍，而推理时间比经过验证的鲁棒性文献中使用的模型少2-4倍。我们在包括MNIST，CIFAR，SVHN和相关的行人检测数据集在内的各种模型架构和数据集中进行的全面评估，展示了VericSpress与当前标准相比，vericspress识别具有减少的压缩验证的核能固定模型的能力。这强调了其作为最终用户的宝贵工具的潜力，例如边缘或物联网平台上的安全至关重要应用程序的开发人员，使他们有能力为各自域中的安全性，批判性，资源受限的平台创建合适的模型。
AI's widespread integration has led to neural networks (NNs) deployment on edge and similar limited-resource platforms for safety-critical scenarios. Yet, NN's fragility raises concerns about reliable inference. Moreover, constrained platforms demand compact networks. This study introduces VeriCompress, a tool that automates the search and training of compressed models with robustness guarantees. These models are well-suited for safety-critical applications and adhere to predefined architecture and size limitations, making them deployable on resource-restricted platforms. The method trains models 2-3 times faster than the state-of-the-art approaches, surpassing relevant baseline approaches by average accuracy and robustness gains of 15.1 and 9.8 percentage points, respectively. When deployed on a resource-restricted generic platform, these models require 5-8 times less memory and 2-4 times less inference time than models used in verified robustness literature. Our comprehensive evaluation across various model architectures and datasets, including MNIST, CIFAR, SVHN, and a relevant pedestrian detection dataset, showcases VeriCompress's capacity to identify compressed verified robust models with reduced computation overhead compared to current standards. This underscores its potential as a valuable tool for end users, such as developers of safety-critical applications on edge or Internet of Things platforms, empowering them to create suitable models for safety-critical, resource-constrained platforms in their respective domains.
