Meet the Walkers: Accelerating Index Traversals for In-Memory Databases

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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 ...

Side-channel disassembly attacks recover CPU instructions from power or electromagnetic side-channel traces measured during code execution. These attacks typically rely on physical access, proximity to the victim device, and high sampling rate measuring in ...

Embedded systems confront two opposite goals: low-power operation and high performance. The current trend to reach these goals is toward heterogeneous platforms, including multi-core architectures with heterogeneous cores and hardware accelerators. The lat ...

Accessing input data is a critical operation in data analytics: i) slow data access significantly degrades performance, and ii) storing everything in the fastest medium, i.e., memory, incurs high operational and hardware costs. Further, while GPUs offer in ...

Lawful Interception (LI) is a legal obligation of Communication Service Providers (CSPs) to provide interception capabilities to Law Enforcement Agencies (LEAs) in order to gain insightful data from network communications for criminal proceedings, e.g., ne ...

In the current era of big data, aggregation queries on high-dimensional datasets are frequently utilized to uncover hidden patterns, trends, and correlations critical for effective business decision-making. Data cubes facilitate such queries by employing p ...

A programmable patient-customized epileptic seizure detector is proposed in this paper to enable neurologists and patients to have constructive interactions with the implantable medical device. The programmability feature is enabled by designing a low-powe ...

Modern data-driven science often consists of iterative cycles of data discovery, acquisition, preparation, analysis, model building and validation leading to knowledge discovery as well as dissemination at scale. The unique challenges of building and simul ...

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 ...

Meet the Walkers: Accelerating Index Traversals for In-Memory Databases

Chattez avec Graph Search

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

Instruction-Level Power Side-Channel Leakage Evaluation of Soft-Core CPUs on Shared FPGAs

Acceleration of Control Intensive Applications on Coarse-Grained Reconfigurable Arrays for Embedded Systems

HetCache: Synergising NVMe Storage and GPU acceleration for Memory-Efficient Analytics

P3LI5: Practical and confidEntial Lawful Interception on the 5G core

Interactive-time Exploration, Querying, and Analysis of Large High-dimensional Datasets

Programmable Seizure Detector Using a 32-bit RISC Processor for Implantable Medical Devices

Blue Brain Nexus: An open, secure, scalable system for knowledge graph management and data-driven science

Privatized graph federated learning