Storage and retrieval of data in a computer memory plays a major role in system performance. Traditionally, computer memory organization is static - i.e., they do not change based on the application-specific characteristics in memory access behaviour during system operation. Specifically, the association of a data block with a search pattern (or cues) as well as the granularity of a stored data do not evolve. Such a static nature of computer memory, we observe, not only limits the amount of data we can store in a given physical storage, but it also misses the opportunity for dramatic performance improvement in various applications. On the contrary, human memory is characterized by seemingly infinite plasticity in storing and retrieving data - as well as dynamically creating/updating the associations between data and corresponding cues. In this paper, we introduce Neural Storage (NS), a brain-inspired learning memory paradigm that organizes the memory as a flexible neural memory network. In NS, the network structure, strength of associations, and granularity of the data adjust continuously during system operation, providing unprecedented plasticity and performance benefits. We present the associated storage/retrieval/retention algorithms in NS, which integrate a formalized learning process. Using a full-blown operational model, we demonstrate that NS achieves an order of magnitude improvement in memory access performance for two representative applications when compared to traditional content-based memory.

中文翻译：

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神经存储：弹性内存的新范例

在计算机内存中存储和检索数据在系统性能中起着重要作用。传统上，计算机内存组织是静态的-即，它们不会根据系统操作期间内存访问行为中的特定于应用程序的特征进行更改。具体而言，数据块与搜索模式（或提示）的关联以及所存储数据的粒度不会演变。我们观察到，计算机内存的这种静态性质不仅限制了我们可以存储在给定物理存储中的数据量，而且还错过了在各种应用程序中显着提高性能的机会。相反，人类记忆的特征在于在存储和检索数据方面看似无限的可塑性-以及动态创建/更新数据与相应提示之间的关联。在本文中，我们介绍了神经存储（NS），这是一种受大脑启发的学习记忆范例，将记忆组织为灵活的神经记忆网络。在NS中，网络结构，关联强度和数据粒度在系统运行期间不断调整，从而提供了前所未有的可塑性和性能优势。我们在NS中介绍了相关的存储/检索/保留算法，该算法集成了形式化的学习过程。使用成熟的操作模型，我们证明与传统的基于内容的内存相比，NS对于两个代表性应用程序的内存访问性能提高了一个数量级。关联强度和数据粒度在系统运行期间不断调整，从而提供了前所未有的可塑性和性能优势。我们在NS中介绍了相关的存储/检索/保留算法，该算法集成了形式化的学习过程。使用成熟的操作模型，我们证明与传统的基于内容的内存相比，NS对于两个代表性应用程序的内存访问性能提高了一个数量级。关联强度和数据粒度在系统运行期间不断调整，从而提供了前所未有的可塑性和性能优势。我们在NS中介绍了相关的存储/检索/保留算法，该算法集成了形式化的学习过程。使用成熟的操作模型，我们证明与传统的基于内容的内存相比，NS对于两个代表性应用程序的内存访问性能提高了一个数量级。