HDTest：脑启发多维计算的差分模糊测试
HDTest: Differential Fuzz Testing of Brain-Inspired Hyperdimensional Computing
摘要：
脑启发超维计算（Brain-inspired hyperdimensional computing，HDC）是一种新兴的计算范式，其模仿大脑认知并利用具有完全分布式全息表示和（伪）随机性的超维向量。与其他机器学习（ML）方法（如深度神经网络（DNN））相比，HDC提供了几个优点，包括高能效，低延迟和一次性学习，使其成为广泛应用的有前途的替代候选者。然而，HDC模型的可靠性和鲁棒性尚未得到探索。在本文中，我们设计，实现和评估HDTest测试HDC模型自动暴露意外或不正确的行为下罕见的输入。HDTest的核心思想是基于引导差分模糊测试。根据HDC中查询超向量和参考超向量之间的距离，HDTest不断地改变原始输入以生成新的输入，这些输入可以触发HDC模型的错误行为。与传统的ML测试方法相比，HDTest不需要手动标记原始输入。以手写数字分类为例，我们证明了HDTest可以生成数千个对抗性输入，这些输入具有可以忽略不计的扰动，可以成功地欺骗HDC模型。平均而言，HDTest可以在商品计算机上运行一分钟内生成大约400个对抗性输入。最后，通过使用HDTest生成的输入来重新训练HDC模型，我们可以增强HDC模型的鲁棒性。据我们所知，本文提出了第一次系统地测试这种新兴的大脑启发的计算模型的努力。
英文摘要：
Brain-inspired hyperdimensional computing (HDC) is an emerging computational paradigm that mimics brain cognition and leverages hyperdimensional vectors with fully distributed holographic representation and (pseudo)randomness. Compared to other machine learning (ML) methods such as deep neural networks (DNNs), HDC offers several advantages including high energy efficiency, low latency, and one-shot learning, making it a promising alternative candidate on a wide range of applications. However, the reliability and robustness of HDC models have not been explored yet. In this paper, we design, implement, and evaluate HDTest to test HDC model by automatically exposing unexpected or incorrect behaviors under rare inputs. The core idea of HDTest is based on guided differential fuzz testing. Guided by the distance between query hypervector and reference hypervector in HDC, HDTest continuously mutates original inputs to generate new inputs that can trigger incorrect behaviors of HDC model. Compared to traditional ML testing methods, HDTest does not need to manually label the original input. Using handwritten digit classification as an example, we show that HDTest can generate thousands of adversarial inputs with negligible perturbations that can successfully fool HDC models. On average, HDTest can generate around 400 adversarial inputs within one minute running on a commodity computer. Finally, by using the HDTest-generated inputs to retrain HDC models, we can strengthen the robustness of HDC models. To the best of our knowledge, this paper presents the first effort in systematically testing this emerging brain-inspired computational model.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
