Quantifying the Mismatch between Emerging Scale-Out Applications and Modern Processors

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 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 ...

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 advancement of face recognition technology has delivered substantial societal advantages. However, it has also raised global privacy concerns due to the ubiquitous collection and potential misuse of individuals' facial data. This presents a notable par ...

The invention relates to an Imaging sensor device in a stacked arrangement comprising: - a pixel array tier comprising a plurality of pixel segments each having a plurality of pixels for photon detection each providing a digital pixel output; - a processin ...

The scale and pervasiveness of the Internet make it a pillar of planetary communication, industry and economy, as well as a fundamental medium for public discourse and democratic engagement. In stark contrast with the Internet's decentralized infrastructur ...

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 ...

Embedded memories occupy an increasingly dominant part of the area and power budgets of modern systems-on-chips (SoCs). Multi-ported embedded memories, commonly used by media SoCs and graphical processing units, occupy even more area and consume higher pow ...

In the past decades, a significant increase of the transistor density on a chip has led to exponential growth in computational power driven by Moore's law. To overcome the bottleneck of traditional von-Neumann architecture in computational efficiency, effo ...

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 ...

Situational awareness strategies are essential for the reliable and secure operation of the electric power grid which represents critical infrastructure in modern society. With the rise of converter-interfaced renewable generation and the consequent shift ...

How to measure students' Computational Problem-Solving (CPS) competencies is an ongoing research topic. Prevalent approaches vary by measurement tools (e.g., interactive programming, multiple-choice tests, or programming-independent tests) and task types ( ...

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 ...

Machine learning and data processing algorithms have been thriving in finding ways of processing and classifying information by exploiting the hidden trends of large datasets. Although these emerging computational methods have become successful in today's ...

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 ...

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 ...

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 ...

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 ...

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 ...

Memory devices have returned to the spotlight due to increasing interest in using in-memory computing architectures to make data-driven algorithms more energy-efficient. One of the main advantages of this architecture is the efficient performance of vector ...

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 ...

Quantifying the Mismatch between Emerging Scale-Out Applications and Modern Processors

Chattez avec Graph Search

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

EdgeAI-Aware Design of In-Memory Computing Architectures

PRO-Face C: Privacy-Preserving Recognition of Obfuscated Face via Feature Compensation

Imaging sensor device using an array of single-photon avalanche diode photodetectors

Planetary-Scale Byzantine Fault Tolerance

Is the powersave governor really saving power?

Multi-Ported GC-eDRAM Bitcell with Dynamic Port Configuration and Refresh Mechanism

Towards Scalable Electronics: Synthetic 2D Materials for Large-Area 2D Circuit Integration

Highly Parallel RTL Simulation

Functional-Basis Analysis of Non-Stationary Signals in Modern Power Grids: Theory and Implementation in Embedded Systems

A Comparative Analysis of Tools & Task Types for Measuring Computational Problem-Solving

Rebooting Virtual Memory with Midgard

2D Nanosystems: Applications of 2D Semiconductors for In-Memory Computing

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

Privatized graph federated learning

Chaosity: Understanding Contemporary NUMA-architectures

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

HPCache: memory-efficient OLAP through proportional caching revisited

How to Achieve Large-Area Ultra-Fast Operation of MoS 2 Monolayer Flash Memories?

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