As deep neural networks (DNNs) continue their reach into a wide range of application
domains, the neural network architecture of DNN models becomes an increasingly sensitive …

As neural networks continue their reach into nearly every aspect of software operations, the
details of those networks become an increasingly sensitive subject. Even those that deploy …

Memory disaggregation is a promising direction to mitigate memory contention in
datacenters. To make memory disaggregation practical, prior efforts expose remote memory …

Understanding susceptibility to Rowhammer bit flips in DRAM (ie, main memory) is vital to
ensure reliability and security on today's systems, particularly on multitenant cloud servers …

Deep learning models are a valuable “secret sauce” that confers a significant competitive
advantage. Many models are never visible to the user and even publicly known state-of-the …

Memory monitoring is of critical use in understanding applications and evaluating systems.
Due to the dynamic nature in programs' memory accesses, common practice today leaves …

In a memory system, understanding how the host is stressing the memory is important to
improve memory performance. Accordingly, the need for the analysis of memory command …

For memory-bound applications, memory bandwidth utilization and memory access latency
determine performance. DRAM specifications mention the maximum peak bandwidth and …

The virtual memory subsystem (VMS) is a long-standing and integral part of an operating
system (OS). It plays a vital role in enabling remote memory systems over fast data center …

Disaggregated memory introduces a cost-effective solution for improving the memory
utilization rate of data centers, by sharing a distributed memory pool among several …