The growing volume of data produced continuously in the Cloud and at the Edge poses significant challenges for large-scale AI applications to extract and learn useful information from the data in a timely and efficient way. The goal of this article is to explore the use of computational storage to address such challenges by distributed near-data processing. We describe Newport, a high-performance and energy-efficient computational storage developed for realizing the full potential of in-storage processing. To the best of our knowledge, Newport is the first commodity SSD that can be configured to run a server-like operating system, greatly minimizing the effort for creating and maintaining applications running inside the storage. We analyze the benefits of using Newport by running complex AI applications such as image similarity search and object tracking on a large visual dataset. The results demonstrate that data-intensive AI workloads can be efficiently parallelized and offloaded, even to a small set of Newport drives with significant performance gains and energy savings. In addition, we introduce a comprehensive taxonomy of existing computational storage solutions together with a realistic cost analysis for high-volume production, giving a good big picture of the economic feasibility of the computational storage technology.

中文翻译：

---

适用于大规模人工智能应用的经济高效、节能且可扩展的存储计算

在云端和边缘不断产生的不断增长的数据量对大规模人工智能应用程序从数据中及时有效地提取和学习有用信息提出了重大挑战。本文的目标是探索使用计算存储来通过分布式近数据处理来应对此类挑战。我们描述了 Newport，这是一种高性能和高能效的计算存储，旨在实现存储处理的全部潜力。据我们所知，Newport 是第一款可以配置为运行类似服务器的操作系统的商品 SSD，极大地减少了创建和维护在存储中运行的应用程序的工作量。我们通过在大型视觉数据集上运行复杂的 AI 应用程序（例如图像相似性搜索和对象跟踪）来分析使用 Newport 的好处。结果表明，数据密集型 AI 工作负载可以有效地并行化和卸载，甚至可以转移到一小组 Newport 驱动器，从而显着提高性能并节省能源。此外，我们介绍了现有计算存储解决方案的综合分类以及大批量生产的实际成本分析，从而很好地了解了计算存储技术的经济可行性。