Search: “hardware”

arXiv:2607.21553v1 Announce Type: new Abstract: We introduce SANA-Video 2.0, a hybrid video diffusion transformer instantiated at 5B and 14B scales under a unified architecture. Designed to generate h

arXiv:2607.18349v1 Announce Type: new Abstract: Objective: To develop a low-cost automated cataract severity classification system operating on standard consumer-grade colour photographs of the eye, w

arXiv:2607.19490v1 Announce Type: cross Abstract: Peer-to-peer distributed inference executes a Large Language Model (LLM) on pooled consumer hardware by spreading its layers across many nodes. Every

arXiv:2607.19661v1 Announce Type: cross Abstract: Several edge computing platforms, such as autonomous vehicles and smart sensing devices, need to adapt to dynamic environments in real time by learnin

arXiv:2607.19456v1 Announce Type: cross Abstract: We derive four memory-optimal inference artifacts for transformer attention using the Mathematics of Arrays (MoA), each following directly from the fo

arXiv:2607.13808v1 Announce Type: new Abstract: Recent extensions of 3D Gaussian Splatting (3DGS) capture fine color details using hash-grid-based appearance parameterization but incur high computatio

arXiv:2304.03388v2 Announce Type: replace Abstract: Deep Neural Networks (DNNs) have become ubiquitous for their ability to solve problems across various domains, including computer vision, natural la

arXiv:2607.06922v1 Announce Type: cross Abstract: Deep learning applications have been widely adopted on edge devices, to mitigate the privacy and latency issues of accessing cloud servers. Deciding t

arXiv:2607.03131v1 Announce Type: cross Abstract: Modern video surveillance systems generate far more video streams than human operators can effectively monitor, making automated analysis essential fo

arXiv:2607.04440v1 Announce Type: cross Abstract: Seismic wavefield simulation is fundamental to seismology, but conventional finite-difference (FD) methods remain limited by numerical dispersion and

arXiv:2607.03012v1 Announce Type: cross Abstract: Video Diffusion Transformers (VDiTs) have demonstrated significant capabilities in high-fidelity video generation. However, their ability to produce l

arXiv:2607.02603v1 Announce Type: cross Abstract: The stable coloring of the Weisfeiler-Leman (1-WL) test is a cornerstone of Graph Neural Networks because it provides an upper bound to the expressive

arXiv:2607.03411v1 Announce Type: cross Abstract: Jamming and spoofing threaten wireless and satellite navigation by disrupting or manipulating radio frequency (RF) signals, undermining availability,

arXiv:2607.04443v1 Announce Type: cross Abstract: We present Wan-Streamer v0.2, a latency-preserving upgrade of the native-streaming, end-to-end audio-visual interaction model. v0.2 keeps the v0.1 mod

arXiv:2607.01646v1 Announce Type: new Abstract: State-of-the-art large language model (LLM) training takes tens of thousands of graphics processing units (GPUs) for months and encounters failures acro

arXiv:2607.01409v1 Announce Type: cross Abstract: GPU training jobs fail often, roughly two in five on large production clusters, yet the operator typically learns of a failure only by reconnecting ho

arXiv:2606.30949v1 Announce Type: new Abstract: High-Level Synthesis (HLS) provides a fast path from concepts to silicon, but converting real-world software into synthesizable HLS code remains challen

arXiv:2606.30709v1 Announce Type: cross Abstract: Hierarchical Global Attention (HGA) is a drop-in replacement for dense causal attention in pretrained long-context transformers. HGA preserves the ori

arXiv:2606.26758v1 Announce Type: new Abstract: High-performance GPU kernels are critical for reducing the exponentially growing computational costs of large language models (LLMs), but their developm

arXiv:2606.26383v1 Announce Type: cross Abstract: How fast could a deep-learning model run on target hardware, and how far is today's implementation from that limit? These questions are central to sof

arXiv:2606.24369v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a dominant post-training paradigm, driving the emergence of high-performance RL systems such as veRL for autoregr

arXiv:2606.24075v1 Announce Type: cross Abstract: Although deep learning-based methods can achieve high accuracy in automatic modulation recognition (AMR) tasks, their high computational cost makes it

arXiv:2606.24039v1 Announce Type: new Abstract: Robotics increasingly relies on GPUs for parallel simulation, large-scale learning, and neural-network inference. For model predictive control (MPC) to

arXiv:2606.22939v1 Announce Type: cross Abstract: Radio frequency jamming poses a critical threat to the availability of wireless Industrial Internet of Things (IIoT) networks. Existing detection and

arXiv:2606.21448v1 Announce Type: new Abstract: As large language models scale, memory bandwidth for key-value caches and retrieval-augmented generation systems becomes a critical bottleneck. While 1-

arXiv:2606.20712v1 Announce Type: new Abstract: Sampling-based Model Predictive Control (SMPC) is a promising strategy for contact-rich robotic manipulation, combining gradient-free optimization with

arXiv:2606.22296v1 Announce Type: new Abstract: Edge Internet of Things (IoT) agents are often constrained by memory capacity, privacy requirements, communication latency, and recurring inference cost

arXiv:2512.22219v2 Announce Type: replace-cross Abstract: We introduce Mirage Persistent Kernel (MPK), the first compiler and runtime system that automatically transforms multi-GPU model inference int

arXiv:2606.09516v1 Announce Type: new Abstract: Real-time video restoration (VR) for live streams requires high-resolution outputs under strict per-frame latency constraints. Existing one-step diffusi

arXiv:2606.09312v1 Announce Type: new Abstract: Tensor program optimization is essential for modern machine learning systems, but its search space is enormous. Existing auto-schedulers reduce measurem

arXiv:2601.16622v2 Announce Type: replace-cross Abstract: Equivariant Graph Neural Networks (EGNNs) have become a widely used approach for modeling 3D atomistic systems. However, mainstream architectu

arXiv:2606.06159v1 Announce Type: cross Abstract: Spiking neural networks (SNNs) have the potential to emerge as the third generation of neural networks and have attracted increasing attention across

arXiv:2606.05687v1 Announce Type: new Abstract: In humanoid motion control, model predictive control (MPC) offers physically grounded prediction and constraint handling, while reinforcement learning (

arXiv:2606.05186v1 Announce Type: cross Abstract: Budget-constrained micro-pretraining often requires triaging many candidate recipes on a shared accelerator before larger search budgets are spent. We

arXiv:2606.06130v1 Announce Type: new Abstract: As underwater robotics research increasingly addresses complex 3D perception and autonomous navigation, the fidelity of sonar simulation has become a ke

arXiv:2606.02781v1 Announce Type: cross Abstract: Deep neural networks (DNNs) have achieved state-of-the-art performance across diverse domains. However, typical Von Neumann compute paradigms face sev

arXiv:2606.01774v1 Announce Type: cross Abstract: Autoregressive (AR) large language models (LLMs) have achieved broad practical success, but sequential decoding remains a key bottleneck for low-laten

arXiv:2606.00516v1 Announce Type: new Abstract: Mixed batching (MB)--interleaving prefill and decode in a single batch--has become the standard scheduling strategy for large language model (LLM) infer

arXiv:2603.09221v2 Announce Type: replace Abstract: Associative memory has long underpinned the design of sequential models. Beyond recall, humans reason by projecting future states and selecting goal

arXiv:2605.31500v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) are bottlenecked by sparse, irregular memory access. Popular frameworks such as DGL and PyTorch Geometric support general

arXiv:2605.30358v1 Announce Type: new Abstract: Quantum computing remains in the Noisy Intermediate-Scale Quantum (NISQ) era, where the performance is highly constrained to noise. Addressing the limit

arXiv:2605.30075v1 Announce Type: new Abstract: Quantum Federated Learning (QFL) offers a promising framework to train quantum models across distributed clients while keeping data strictly local. Due

arXiv:2605.30345v1 Announce Type: new Abstract: Printed circuit board (PCB) schematic design defines nearly all electronic hardware, but it remains manual and expertise-intensive. While generative AI

arXiv:2605.27472v1 Announce Type: cross Abstract: Assertion-based verification (ABV) is a cornerstone of modern hardware design, yet manually translating design intent into formal SystemVerilog Assert

arXiv:2605.25346v1 Announce Type: cross Abstract: Neural network (NN) dynamics models and control policies achieve strong performance in robotics, but providing sound guarantees under uncertainty rema

arXiv:2605.24016v1 Announce Type: cross Abstract: Diffusion inference remains costly for edge deployment, yet existing accelerators focus almost exclusively on score networks because standard drift is