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The end of scaling from Moore’s and Dennard’s laws has greatly slowed improvements in CPU speed, RAM capacity, and disk/flash capacity. Meanwhile, cloud database systems, which are the backbone for many large-scale services and applications in the cloud, are continuing to grow exponentially. For example, most of Google’s products that run on the Spanner database have more than a billion users and are continuously growing. Moreover, the growth in data also shows no signs of slowing down, with further orders-of-magnitude increases likely, due to autonomous vehicles, the internet-of-things, and human-driven data creation. Meanwhile, machine learning creates an appetite for data that also needs to be preprocessed using scalable cloud database systems. As a result, cloud database systems are facing a fundamental scalability wall on how to further support this exponential growth given the stagnation in hardware. While database research has a long tradition of investigating how modern hardware can be leveraged to improve overall system performance - which is also shown by the series of past Dagstuhl Seminars - a more holistic view is required to address the imminent exponential scalability challenge that databases will be facing. However, applying hardware accelerators in a database needs a careful design. In fact, so far, no commercial system has applied hardware accelerators at scale. Unlike other hyper-scale applications such as machine learning training and video processing where accelerators such as GPUs and TPUs circumvent this problem, workloads in cloud database systems are typically not compute-bound and thus benefit less or not at all from such existing accelerators. This Dagstuhl Seminar thus aimed to bring together leading researchers and practitioners from database systems, hardware architecture, and storage systems to rethink, from the ground up, how to co-design database systems and compute/storage hardware. By uniting experts across these disciplines, the seminar sought to identify the architectural changes and system designs that could enable the order-of-magnitude improvements required for the next generation of applications.