人工智能（AI）对计算的巨大需求正在推动对AI的硬件和软件系统的无与伦比的投资。这导致了专用硬件设备数量的爆炸，现在由主要的云供应商提供。通过通过基于张量的界面隐藏低级复杂性，张量计算运行时间（TCR）（例如Pytorch）允许数据科学家有效利用新硬件提供的令人兴奋的功能。在本文中，我们探讨了数据库管理系统如何在AI空间中乘坐创新浪潮。我们设计，构建和评估张量查询处理器（TQP）：TQP将SQL查询转换为张量程序，并在TCR上执行它们。 TQP能够通过在张量例程中实现与关系运算符的新颖算法来运行完整的TPC-H基准。同时，TQP可以支持各种硬件，而仅需要通常的开发工作。实验表明，与专用CPU和仅GPU的系统相比，TQP可以将查询执行时间提高到10美元$ \ times $。最后，TQP可以加速查询ML预测和SQL端到端，并在CPU基线上输送高达9 $ \ times $速度。

We designed and constructed an A-sized base autonomous underwater vehicle (AUV), augmented with a stack of modular and extendable hardware and software, including autonomy, navigation, control and high fidelity simulation capabilities (A-size stands for the standard sonobuoy form factor, with a maximum diameter of 124 mm). Subsequently, we extended this base vehicle with a novel tuna-inspired morphing fin payload module (referred to as the Morpheus AUV), to achieve good directional stability and exceptional maneuverability; properties that are highly desirable for rigid hull AUVs, but are presently difficult to achieve because they impose contradictory requirements. The morphing fin payload allows the base AUV to dynamically change its stability-maneuverability qualities by using morphing fins, which can be deployed, deflected and retracted, as needed. The base vehicle and Morpheus AUV were both extensively field tested in-water in the Charles river, Massachusetts, USA; by conducting hundreds of hours of operations over a period of two years. The maneuvering capability of the Morpheus AUV was evaluated with and without the use of morphing fins to quantify the performance improvement. The Morpheus AUV was able to showcase an exceptional turning rate of around 25-35 deg/s. A maximum turn rate improvement of around 35% - 50% was gained through the use of morphing fins.