The increasing demand on the main memory capacity is one of the main big data
challenges. Dynamic random access memory (DRAM) does not represent the best choice …

Computer systems utilizing byte-addressable Non-Volatile Memory (NVM) as
memory/storage can provide low-latency data persistence. The widely used key-value stores …

Edge computing is proposed to bridge mobile devices with cloud computing data centers in
the era of mobile big data, as an intermediate level of computing power. One important issue …

Fog computing requires a large main memory capacity to decrease latency and increase the
Quality of Service (QoS). However, dynamic random access memory (DRAM), the commonly …

Non-Volatile Memory (NVM) technology is a promising solution to fulfill the ever-growing
need for higher capacity in the main memory of modern systems. Despite having many great …

Current DRAM based memory systems face the scalability challenges in terms of storage
density, power, and cost. Hybrid memory architecture composed of emerging Non-Volatile …

Hybrid main memory with both DRAM and emerging non-volatile memory (NVM) becomes a
promising solution for high performance and energy-efficient embedded systems. Cache …

For the deep neural network applications, the requirement of the memory bandwidth and
volume is huge. Multiple DRAM and PCM (phase-change memory) chips are stacked to form …

The traditional dynamic random-access memory (DRAM) storage medium can be integrated
on chips via modern emerging 3D-stacking technology to architect a DRAM shared cache in …

Current DRAM-based memory systems face the scalability challenges in terms of memory
density, energy consumption, and monetary cost. Hybrid memory architectures composed of …