X-Stream: Edge-centric Graph Processing using Streaming Partitions

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Information derived from experiences is incorporated into the brain as changes to ensembles of cells, termed engram cells, which allow memory storage and recall. The mechanism by which those changes hold specific information is unclear. Here, we test the h ...

By supporting the access of multiple memory words at the same time, Bit-line Computing (BC) architectures allow the parallel execution of bit-wise operations in-memory. At the array periphery, arithmetic operations are then derived with little additional o ...

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for dimensionality reduction of time-series using contrastive learning. A method can include receiving multidimensional input time series data that includes ...

Maximal subgraph mining is increasingly important in various domains, including bioinformatics, genomics, and chemistry, as it helps identify common characteristics among a set of graphs and enables their classification into different categories. Existing ...

This work presents a graph neural network (GNN) framework for solving the maximum independent set (MIS) problem, inspired by dynamic programming (DP). Specifically, given a graph, we propose a DP-like recursive algorithm based on GNNs that firstly construc ...

Most modern in-memory online transaction processing (OLTP) engines rely on multi-version concurrency control (MVCC) to provide data consistency guarantees in the presence of conflicting data accesses. MVCC improves concurrency by generating a new version o ...

Modern data management systems aim to provide both cutting-edge functionality and hardware efficiency. With the advent of AI-driven data processing and the post-Moore Law era, traditional memory-bound scale-up data management operations face scalability ch ...

Many sports leagues organize their competitions as round-robin tournaments. This tournament design has a rich mathematical structure that has been studied in the literature over the years. We review some of the main properties and fundamental scheduling me ...

Federated learning is a semi-distributed algorithm, where a server communicates with multiple dispersed clients to learn a global model. The federated architecture is not robust and is sensitive to communication and computational overloads due to its one-m ...

High performance computing (HPC) servers aim to meet an increase in the number and complexity of tasks and, consequently, to address the energy efficiency challenge. In addition to energy efficiency, it is essential to manage lifetime limitations of power- ...

The foreseen important increase of the penetration of distributed renewable energies technologies into the electricity distribution grid is expected to lead to some major challenges. On the one hand, a large and synchronous production could lead in certain ...

X-Stream: Edge-centric Graph Processing using Streaming Partitions

Chat with Graph Search

Engram cell connectivity as a mechanism for information encoding and memory function

Bit-Line Computing for CNN Accelerators Co-Design in Edge AI Inference

Dimensionality reduction of time-series data, and systems and devices that use the resultant embeddings

Scalable maximal subgraph mining with backbone-preserving graph convolutions

Maximum Independent Set: Self-Training through Dynamic Programming

One-shot Garbage Collection for In-memory OLTP through Temporality-aware Version Storage

Post-Moore's Law Fusion: High-Bandwidth Memory, Accelerators, and Native Half-Precision Processing for CPU-Local Analytics

A tutorial on graph models for scheduling round-robin sports tournaments

Privatized graph federated learning

COCKTAIL: Multi-Core Co-Optimization Framework With Proactive Reliability Management

SCCER JA-RED - Report on the performance of the implemented solutions with guidelines for subsequent implementation - D1.2.4