  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.
