Scale-Out Processors

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The escalating computational and memory demands of deep neural networks have outpaced chip density improvements, making arithmetic density a key bottleneck for GPUs. Scaled numeric formats, such as FP8 and Microscaling (MX), improve arithmetic density by a ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

Magnonics is a research field that has gained an increasing interest in both the fundamental and applied sciences in recent years. This field aims to explore and functionalize collective spin excitations in magnetically ordered materials for modern informa ...

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 ...

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 ...

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 ...

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 ...

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 ...

Vision systems built around conventional image sensors have to read, encode and transmit large quantities of pixel information, a majority of which is redundant. As a result, new computational imaging sensor architectures were developed to preprocess the r ...

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 ...

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 ...

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 ...

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 ...

Scale-Out Processors

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Avant-Garde: Empowering GPUs with Scaled Numeric Formats

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

Highly Parallel RTL Simulation

EdgeAI-Aware Design of In-Memory Computing Architectures

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

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

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

Is the powersave governor really saving power?

The 2024 Magnonics Roadmap

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

uKharon: A Membership Service for Microsecond Applications

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

HPCache: memory-efficient OLAP through proportional caching revisited

Rebooting Virtual Memory with Midgard

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

Computational Imaging SPAD Cameras

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

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

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

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