Search: “hardware”

arXiv:2609.12218v1 Announce Type: cross Abstract: The growing use of electroencephalography (EEG) motivates automated analysis that is accurate, transferable, and deployable on constrained hardware. R

arXiv:2609.04557v1 Announce Type: cross Abstract: Quantum error-correction decoders are typically benchmarked against synthetic circuit-level noise, under the assumption that a decoder's ranking under

Introducing Fleet: GPU benchmarking entirely in your browser. Run WebGPU compute kernels drawn from real AI workloads directly on your hardware and earn a personalized card built for your device. Rate

arXiv:2602.00704v2 Announce Type: replace Abstract: The generation of Register-Transfer Level (RTL) code is a crucial yet labor-intensive step in digital hardware design, traditionally requiring engin

arXiv:2608.21806v1 Announce Type: cross Abstract: Computational resources are increasingly central to NLP research, but how closely reported GPU capability aligns with scholarly impact remains unclear

arXiv:2608.21555v1 Announce Type: cross Abstract: Modern machine learning compilers select tensor memory layouts to minimize execution cost under hardware constraints. Layout selection is global: an o

NVIDIA’s Spectrum‑X Ethernet combines hardware‑accelerated adaptive routing, targeted congestion control and NIC‑based plane load balancing to meet the extreme bandwidth and low‑latency demands of gig

arXiv:2608.21157v1 Announce Type: cross Abstract: High-performance GPU kernels underpin modern deep learning and scientific computing. As workloads become increasingly diverse and GPU hardware evolves

Artificial intelligence hardware startup Starcloud Inc. today announced that it has raised 250 million in funding at a 2.3 billion valuation. Investment firm Manhattan West led the deal. It was joined

This post was made with AI. I tried to remove as much slop as possible and keep it straight to the point to save your time as I know how annoying AI slop posts can be, but I still wanted to retain all

arXiv:2608.15297v1 Announce Type: new Abstract: Real-time 6DoF object pose estimation on resource-constrained hardware remains challenging, as accurate correspondence-based and refinement pipelines ty

hey again! I run a EU PC hardware price tracker PriceSquirrel, 25+ stores across 9 countries, and wanted to check: are GPU prices actually rising, or does it just feel that way? To make this defensibl

I'm trying to figure out GPU Kernel optimization on older hardware like SM80(ampere) . Is there tools you guys use? Or frameworks? Im waiting for this framework https://www.reddit.com/r/LocalLLaMA/com

arXiv:2603.10711v3 Announce Type: replace Abstract: Real-time solution of nonlinear optimal control problems remains challenging on embedded robotic hardware, where conventional solvers often rely on

arXiv:2608.04173v1 Announce Type: new Abstract: Deep neural networks (DNNs) deployed on resource-constrained neuromorphic hardware face three concurrent challenges: the need for model compression thro

arXiv:2608.02606v1 Announce Type: new Abstract: Fault tolerance in classical computing has traditionally relied on static strategies like hardware redundancy and error-correcting codes. Biological sys

arXiv:2608.02229v1 Announce Type: new Abstract: Classical neural networks frequently produce overconfident predictions on ambiguous or out-of-distribution (OOD) data, a liability that grows with each

It's more accurate to say that many highly engaged online gamers are hostile to AI, not that 'gamers' as a whole are. Gaming is a huge community with hundreds of millions of people, and opinions vary

arXiv:2607.29266v1 Announce Type: cross Abstract: Artificial Intelligence (AI) and Deep Learning (DL) have notably advanced medical image analysis, yet many health- care organizations struggle to adop

Written by: Kelli Vanderlee, Stuart Carrera For years, the cybersecurity industry's understanding of software supply chain compromise has been anchored by a few watershed events, including Russian cyb

arXiv:2607.26855v1 Announce Type: new Abstract: Many scientific fields rely on standard benchmarks and shared platforms to improve review and reproducibility, but autonomous systems research still lac

AI hardware competition entered a sharper phase this week as Advanced Micro Devices Inc. used its flagship AI event to argue it isn’t merely chasing Nvidia Corp. — it intends to lead the market outrig

Devin Outposts are now available on NVIDIA Brev. Write and profile kernels, run experiments, serve and fine-tune OSS models, and test against real hardware. Try it out: https://brev.nvidia.com/launcha

Run Devin Outposts on @nvidia Brev to give Devin access to GPU infrastructure for debugging training runs, inspecting GPU state, reproducing failures, and validating fixes on the hardware where the wo

CUDA 13.3 introduces **clmad**, a hardware‑accelerated carryless multiply‑accumulate instruction available on all NVIDIA Ampere and newer GPUs (SM 80+), filling a long‑standing gap in GPU‑native binar

Integrated circuit and electronic hardware design company Cadence Design Systems Inc. today announced a new artificial intelligence agent that assists with packaging and system design, the step after

arXiv:2607.11985v1 Announce Type: cross Abstract: Hybrid quantum-classical machine learning workflows repeatedly evaluate many small parametrized circuits during training and model exploration. In thi

arXiv:2607.06924v1 Announce Type: new Abstract: On analog neuromorphic hardware, intrinsic device noise is normally an accuracy tax. We ask whether it can instead consolidate memories. We cast per-syn

arXiv:2607.06499v1 Announce Type: new Abstract: With the widespread availability of parallel computing hardware, sampling-based motion planning methods such as Model Predictive Path Integral (MPPI) co

arXiv:2512.23236v4 Announce Type: replace-cross Abstract: Making deep learning recommendation model (DLRM) training and inference fast and efficient is important. However, this presents three key syst

arXiv:2607.02630v1 Announce Type: cross Abstract: Hardware accelerators now sit on the critical path of online serving. GPUs, FPGAs, and increasingly remote services such as hardware security modules,

It's wild how quickly Etched designed and got the chips out, all within 2 years. They went deep, hardcoding attention into silicon and getting very high MFU. This kind of hardware tailored made for LL

arXiv:2606.01680v1 Announce Type: cross Abstract: Network failures are among the most frequent hardware faults in large-scale GPU clusters and a leading cause of training-job interruptions. Modern col

arXiv:2601.22813v2 Announce Type: replace Abstract: The NVFP4 lower-precision format, supported in hardware by NVIDIA Blackwell GPUs, promises to allow, for the first time, end-to-end fully-quantized

arXiv:2605.30359v1 Announce Type: cross Abstract: Generating high-performance GPU kernels remains challenging due to the need for both correctness and hardware-aware optimization. While large language

Gary Marcus discusses declining prices for NVIDIA's H100 and H200 AI chips, suggesting that inflated valuations and returns on investment for AI hardware customers are becoming unsustainable as the ma

arXiv:2605.21560v1 Announce Type: new Abstract: Deploying neural networks on microcontroller units (MCUs) is critical for edge intelligence but remains challenging due to tight memory, storage, and co

EDA Market size, Share, Business Models, Drivers, Changing Customer Base, Competitive Dynamics Across Synopsys, Cadence, and Siemens, China EDA, IP, Hardware, CoT, Lock-In Economics, Disruptive Forces

arXiv:2605.20799v1 Announce Type: cross Abstract: We present Overall FLOP Utilization (OFU), a hardware-level, precision-agnostic GPU efficiency metric for AI workloads on HPC systems, derived from tw

arXiv:2605.20179v1 Announce Type: new Abstract: Diffusion Large Language Models (dLLMs) have emerged as a competitive alternative to autoregressive (AR) models, offering better hardware utilization an

arXiv:2605.19593v1 Announce Type: new Abstract: Modern deployments of Large Language Models (LLMs) increasingly require serving multiple models with diverse architectures, sizes, and specialization on

arXiv:2605.17289v1 Announce Type: cross Abstract: Unstructured sparsity is now natively accelerated by recent GPU kernels and dataflow hardware, shifting the bottleneck from inference execution to the

arXiv:2412.12636v3 Announce Type: replace-cross Abstract: Large-scale ML training jobs are frequently interrupted by hardware and software anomalies, failures, and management events. Existing solution

arXiv:2605.14839v1 Announce Type: new Abstract: Traditional methods for classifying global navigation satellite system (GNSS) jamming signals typically involve post-processing raw or spectral data str

arXiv:2605.07985v1 Announce Type: cross Abstract: Selecting the optimal LLM inference configuration requires evaluation across hardware, serving engines, attention backends, and model architectures, s

Nvidia's stock rose 2.6% following news that xAI is selling 220,000 used GPUs on the secondhand market, potentially indicating a shift in AI hardware demand or xAI's operational priorities. The report