tag: GPU

Probabilistic Memory Architecture That Bridges The Gap Between RNG Sampling and Memory Access (Notre Dame, Georgia Tech, Villanova)
By Technical Paper Link - 03 Jul, 2026 - Comments: 0

Researchers from University of Notre Dame, Georgia Institute of Technology, and Villanova University published a technical paper titled “Probabilistic Memory for Trustworthy Edge Intelligence.” Summary: The paper introduces p-MEM as “a unified memory primitive” that samples at “the native memory bandwidth.” It reports reductions in instruction count, sampling latency, and energy ... » read more

A Massively Parallel GPU Rasterizer for Next-generation Computational Lithography
By Siemens EDA - 01 Jul, 2026 - Comments: 0

Computational lithography, critical for advanced semiconductor manufacturing, demands high-performance rasterization to meet nanometer-scale precision. Traditional CPU-based rasterizers struggle with the increasing complexity and data volumes of modern designs. This paper presents a massively parallel GPU rasterizer designed to accelerate high-resolution mask synthesis, lithography simulation, ... » read more

HyperLane: GPU Virtualization with Imagination
By Imagination Technologies - 01 Jul, 2026 - Comments: 0

GPU virtualization allows multiple operating systems to share a single GPU, each submitting their tasks independently and free from interference. It is a feature that is increasingly in demand in automotive, data center and consumer devices as higher performance platforms are being asked to handle more workloads simultaneously. This fifteen-page white paper introduces the concept of GPU virt... » read more

Agentic AI Is Changing Data Center Architectures
By Ann Mutschler - 11 Jun, 2026 - Comments: 0

Key Takeaways: The rise of agentic AI is shifting data centers from GPU-centric number crunching to CPU-driven orchestration, where managing long-running reasoning loops and context is just as important as raw compute. Integrating CPUs, GPUs, and stacked memory into tightly coupled multi-die architectures with varying workloads makes it much harder to ensure they will be reliable and ef... » read more

HW-Native, GPU Compiler for Large-scale ML Production Systems (UC San Diego, Meta)
By Technical Paper Link - 18 May, 2026 - Comments: 0

A new technical paper, "TLX: Hardware-Native, Evolvable MIMW GPU Compiler for Large-scale Production Environments," was published by researchers at UC San Diego and Meta. Abstract "Modern GPUs increasingly rely on specialized hardware units and asynchronous coordination mechanisms, so performance depends on orchestrating data movement, tensor-core computation, and synchronization rather t... » read more

Replacing GPU Compute Dies With PNM-Enabled HBM Cubes For Long-Context Decode Attention (UCSD, Columbia, Yonsei U., NVIDIA, Samsung)
By Technical Paper Link - 05 May, 2026 - Comments: 0

A new technical paper, "AMMA: A Multi-Chiplet Memory-Centric Architecture for Low-Latency 1M Context Attention Serving," was published by researchers at UC San Diego, Columbia University, Yonsei University, NVIDIA, and Samsung. Abstract "All current LLM serving systems place the GPU at the center, from production-level attention-FFN disaggregation to NVIDIA's Rubin GPU-LPU heterogeneous p... » read more

SSD Emulator For Massively Parallel, GPU-Centric Storage (KAIST)
By Technical Paper Link - 11 Apr, 2026 - Comments: 0

A new technical paper, "SwarmIO: Towards 100 Million IOPS SSD Emulation for Next-generation GPU-centric Storage Systems," was published by KAIST. Abstract "GPU-initiated I/O has emerged as a key mechanism for achieving high-throughput storage access by leveraging massive GPU thread-level parallelism, while recent industry trends point toward SSDs optimized for ultra-high random-read IOPS.... » read more

Systematic Analysis of CPU-Induced Slowdowns in Multi-GPU LLM Inference (Georgia Tech)
By Technical Paper Link - 27 Mar, 2026 - Comments: 0

A new technical paper, "Characterizing CPU-Induced Slowdowns in Multi-GPU LLM Inference," was published by the Georgia Institute of Technology. Abstract "Large-scale machine learning workloads increasingly rely on multi-GPU systems, yet their performance is often limited by an overlooked component: the CPU. Through a detailed study of modern large language model (LLM) inference and servin... » read more

Enabling the Industry’s First GPU-Accelerated Manufacturing Platform
By Synopsys - 19 Feb, 2026 - Comments: 0

Discover how modern chip designs are revolutionizing the lithographic process, driving the need for innovative solutions to meet the industry's demand for shorter design cycles. This whitepaper explores the significant role of GPUs in accelerating computational lithography, offering unprecedented speed-ups for EDA tools in chip development. Learn about the collaborative efforts of Synopsys, NVI... » read more

HW-Triggered Backdoors Across Common GPU Accelerators (BIFOLD, TU Berlin, CISPA)
By Technical Paper Link - 02 Feb, 2026 - Comments: 0

A new technical paper titled "Hardware-Triggered Backdoors" was published by researchers at Berlin Institute for the Foundations of Learning and Data (BIFOLD), TU Berlin and CISPA Helmholtz Center for Information Security. Abstract "Machine learning models are routinely deployed on a wide range of computing hardware. Although such hardware is typically expected to produce identical result... » read more

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