ALLARM: Optimizing Sparse Directories for Thread-Local Data

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

The increasing demand for computing power and the emergence of heterogeneous computing architectures have driven the exploration of innovative techniques to address current limitations in both the compute and memory subsystems. One such solution is the use ...

Driven by the demand for real-time processing and the need to minimize latency in AI algorithms, edge computing has experienced remarkable progress. Decision-making AI applications stand out for their heavy reliance on data-centric operations, predominantl ...

The increasing complexity of transformer models in artificial intelligence expands their computational costs, memory usage, and energy consumption. Hardware acceleration tackles the ensuing challenges by designing processors and accelerators tailored for t ...

Computer systems rely heavily on abstraction to manage the exponential growth of complexity across hardware and software. Due to practical considerations of compatibility between components of these complex systems across generations, developers have favou ...

A frequency scaling governor is critical for the performance management of cloud servers, as it enhances energy efficiency and helps to control operational temperatures, thereby ensuring system reliability. However, our in-depth analysis of the application ...

The ever-increasing computational complexity and energy consumption of today's applications, such as Machine Learning (ML) algorithms, not only strain the capabilities of the underlying hardware but also significantly restrict their wide deployment at the ...

The desire and ability to place AI-enabled applications on the edge has grown significantly in recent years. However, the compute-, area-, and power-constrained nature of edge devices are stressed by the needs of the AI-enabled applications, due to a gener ...

Verification and testing of hardware heavily relies on cycle-accurate simulation of RTL. As single-processor performance is growing only slowly, conventional, single-threaded RTL simulation is becoming impractical for increasingly complex chip designs and ...

The present invention relates to systems and methods for predicting a prognosis of the neuropsychological and/or neuropsychiatric status in a subject based on reports of Minor Hallucination (MH) events in combination with electrophysiological data of the s ...

Modern hardware is increasingly complex, requiring increasing effort to understand in order to carefully engineer systems for optimal performance and effective utilization. Moreover, established design principles and assumptions are not portable to modern ...

Analytical engines rely on in-memory data caching to avoid storage accesses and provide timely responses by keeping the most frequently accessed data in memory. Purely frequency- and time-based caching decisions, however, are a proxy of the expected query ...

The miniaturization of integrated circuits (ICs) and their higher performance and energy efficiency, combined with new machine learning algorithms and applications, have paved the way to intelligent, interconnected edge devices. In the medical domain, they ...

Virtual Memory (VM) is a critical programming abstraction that is widely used in various modern computing platforms. With the rise of datacenter computing and birth of planet-scale online services, the semantic and capacity requirements from memory have ev ...

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 ...

Today's continued increase in demand for processing power, despite the slowdown of Moore's law, has led to an increase in processor count, which has resulted in energy consumption and distribution problems. To address this, there is a growing trend toward ...

Context. A novel high-performance exact pair-counting toolkit called fast correlation function calculator (FCFC) is presented.Aims. With the rapid growth of modern cosmological datasets, the evaluation of correlation functions with observational and simula ...

Modern data center fabrics open the possibility of microsecond distributed applications, such as data stores and message queues. A challenging aspect of their development is to ensure that, besides being fast in the common case, these applications react fa ...

While software applications, programming languages, and hardware have changed, operating systems have not. Widely-used commodity operating systems are still modeled after the ones designed in the seventies. The accumulated burden of backward compatibility ...

The demise of Moore's Law and Dennard scaling has resulted in diminishing performance gains for general-purpose processors, and so has prompted a surge in academic and commercial interest for hardware accelerators. Specialized hardware has already redefine ...

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 ...

ALLARM: Optimizing Sparse Directories for Thread-Local Data

Chattez avec Graph Search

Intermediate Address Space: virtual memory optimization of heterogeneous architectures for cache-resident workloads

EdgeAI-Aware Design of In-Memory Computing Architectures

Accelerator-driven Data Arrangement to Minimize Transformers Run-time on Multi-core Architectures

Secure Interface Design Leveraging Hardware/Software Support

Is the powersave governor really saving power?

An Energy Efficient Soft SIMD Microarchitecture and Its Application on Quantized CNNs

Exploring High-Performance and Energy-Efficient Architectures for Edge AI-Enabled Applications

Highly Parallel RTL Simulation

System for predicting a prognosis of the neuropsychological and/or neuropsychiatric status in a subject

Chaosity: Understanding Contemporary NUMA-architectures

HPCache: memory-efficient OLAP through proportional caching revisited

Exploring brain-inspired multi-core heterogeneous hardware templates for low-power biomedical embedded systems

Rebooting Virtual Memory with Midgard

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

Compilation and Design Space Exploration of Dataflow Programs for Heterogeneous CPU-GPU Platforms

Fast correlation function calculator A high-performance pair-counting toolkit

uKharon: A Membership Service for Microsecond Applications

Dynamic Linkers Are the Narrow Waist of Operating Systems

Building Chips Faster: Hardware-Compiler Co-Design for Accelerated RTL Simulation

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