Related papers: Tiered Object Storage using Persistent Memory

We propose a method for unsupervised video object segmentation by transferring the knowledge encapsulated in image-based instance embedding networks. The instance embedding network produces an embedding vector for each pixel that enables…

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…

Visual object tracking task is constantly gaining importance in several fields of application as traffic monitoring, robotics, and surveillance, to name a few. Dealing with changes in the appearance of the tracked object is paramount to…

SRAM-based cache memory faces several scalability limitations in deep nanoscale technologies, e.g., high leakage current, low cell stability, and low density. Emerging Non-Volatile Memory (NVM) technologies have received lots of attention…

With recent video object segmentation (VOS) benchmarks evolving to challenging scenarios, we revisit a simple but overlooked strategy: restricting the size of memory banks. This diverges from the prevalent practice of expanding memory banks…

The concept of memory disaggregation has recently been gaining traction in research. With memory disaggregation, data center compute nodes can directly access memory on adjacent nodes and are therefore able to overcome local memory…

The exponential growth of artificial intelligence (AI) and machine learning (ML) applications has necessitated the development of efficient storage solutions for vector and tensor data. This paper presents a novel approach for tensor…

Distributed AI systems face critical memory management challenges across computation, communication, and deployment layers. RRAM based in memory computing suffers from scalability limitations due to device non idealities and fixed array…

Many managed key-value and NoSQL databases - such as Amazon DynamoDB, Azure Cosmos DB, and Google Cloud Firestore - enforce strict maximum item sizes (e.g., 400 KB in DynamoDB). This constraint imposes significant architectural challenges…

Modern workloads are demanding increasingly larger memory capacity. Compute Express Link (CXL)-based memory tiering has emerged as a promising solution for addressing this problem by utilizing traditional DRAM alongside slow-tier CXL memory…

Utility mining emerged to overcome the limitations of frequent itemset mining by considering the utility of an item. Utility of an item is based on user's interest or preference. Recently, temporal data mining has become a core technical…

Data prefetching aims to improve access times to data storage systems by predicting data records that are likely to be accessed by subsequent requests and retrieving them into a memory cache before they are needed. In the case of Persistent…

We present a slot-wise, object-based transition model that decomposes a scene into objects, aligns them (with respect to a slot-wise object memory) to maintain a consistent order across time, and predicts how those objects evolve over…

The conventional von Neumann architecture has been revealed as a major performance and energy bottleneck for rising data-intensive applications. %, due to the intensive data movements. The decade-old idea of leveraging in-memory processing…

Object permanence is the concept that objects do not suddenly disappear in the physical world. Humans understand this concept at young ages and know that another person is still there, even though it is temporarily occluded. Neural networks…

Emerging non-volatile main memory (NVRAM) technologies provide byte-addressability, low idle power, and improved memory-density, and are likely to be a key component in the future memory hierarchy. However, a critical challenge in achieving…

The theoretical foundations of a new model and paradigm (called TIE) for data storage and access are introduced. Associations between data elements are stored in a single Matrix table, which is usually kept entirely in RAM for quick access.…

Visual localization is a fundamental task for a wide range of applications in the field of robotics. Yet, it is still a complex problem with no universal solution, and the existing approaches are difficult to scale: most state-of-the-art…

While modern visual recognition systems have made significant advancements, many continue to struggle with the open problem of learning from few exemplars. This paper focuses on the task of object detection in the setting where object…

Motivated by emerging applications to the edge computing paradigm, we introduce a two-layer erasure-coded fault-tolerant distributed storage system offering atomic access for read and write operations. In edge computing, clients interact…