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Typical large-scale recommender systems use deep learning models that are stored on a large amount of DRAM. These models often rely on embeddings, which consume most of the required memory. We present Bandana, a storage system that reduces…

NVMe Flash-based SSDs are widely deployed in data centers to cache working sets of large-scale web services. As data centers face increasing sustainability demands, such as reduced carbon emissions, efficient management of Flash…

Resistance switching random access memory (ReRAM), with the ability to repeatedly modulate electrical resistance, has been highlighted as a feasible high-density memory with the potential to replace negative-AND (NAND) flash memory. Such…

Response time requirements for big data processing systems are shrinking. To meet this strict response time requirement, many big data systems store all or most of their data in main memory to reduce the access latency. Main memory…

Contemporary 3D research, particularly in reconstruction and generation, heavily relies on 2D images for inputs or supervision. However, current designs for these 2D-3D mapping are memory-intensive, posing a significant bottleneck for…

Memory latency, bandwidth, capacity, and energy increasingly limit performance. In this paper, we reconsider proposed system architectures that consist of huge (many-terabyte to petabyte scale) memories shared among large numbers of CPUs.…

The in-memory computing paradigm with emerging memory devices has been recently shown to be a promising way to accelerate deep learning. Resistive processing unit (RPU) has been proposed to enable the vector-vector outer product in a…

Disaggregated memory leverages recent technology advances in high-density, byte-addressable non-volatile memory and high-performance interconnects to provide a large memory pool shared across multiple compute nodes. Due to higher memory…

Intrinsic charge trap capacitive non-volatile flash memories take a significant share of the semiconductor electronics market today. It is a challenge to create intrinsic traps in the dielectric layer without high temperature processing…

Dynamic Random Access Memory (DRAM) is the prevalent memory technology used to build main memory systems of almost all computers. A fundamental shortcoming of DRAM is the need to refresh memory cells to keep stored data intact. DRAM refresh…

To accommodate the growing memory footprints of today's applications, CPU vendors have employed large DRAM caches, backed by large non-volatile memories like Intel Optane (e.g., Intel's Cascade Lake). The existing computer architecture…

As memory technologies continue to shrink and memory error rates increase, the demand for stronger reliability becomes increasingly critical. Fine-grain memory replication has emerged as an appealing approach to improving memory fault…

Deep Neural Networks (DNNs) have emerged as the most effective programming paradigm for computer vision and natural language processing applications. With the rapid development of DNNs, efficient hardware architectures for deploying…

Emerging Non-Volatile Memories (NVMs) are promising contenders for building future memory systems. On the other side, unlike DRAM systems, NVMs can retain data even after power loss and thus enlarge the attack surface. While data encryption…

Negative Biased Temperature Instability (NBTI)-induced aging is one of the critical reliability threats in nano-scale devices. This paper makes the first attempt to study the NBTI aging in the on-chip weight memories of deep neural network…

While containers efficiently implement the idea of operating-system-level application virtualization, they are often insufficient to increase the server utilization to a desirable level. The reason is that in practice many containerized…

The discovery that memory of particle configurations and plastic events can be stored in amorphous solids subject to oscillatory shear has spurred research into methods for storing and retrieving information from these materials. However,…

Ferroelectric memories have attracted significant interest due to their non-volatile storage, energy efficiency, and fast operation, making them prime candidates for future memory technologies. As commercial Dynamic Random Access Memory…

Non-volatile memory (NVM) is a class of promising scalable memory technologies that can potentially offer higher capacity than DRAM at the same cost point. Unfortunately, the access latency and energy of NVM is often higher than those of…

Today's systems are overwhelmingly designed to move data to computation. This design choice goes directly against at least three key trends in systems that cause performance, scalability and energy bottlenecks: (1) data access from memory…