Search: “hardware”

arXiv:2609.20441v1 Announce Type: new Abstract: Geospatial foundation models can provide strong flood-segmentation performance, but their size limits deployment on memory-constrained edge hardware. We

arXiv:2609.19387v1 Announce Type: new Abstract: Agents are increasingly asked to design hardware, and increasingly reported to succeed. Such reports establish that a design improved; they cannot estab

arXiv:2609.19279v1 Announce Type: new Abstract: Running artificial intelligence (AI) models directly on edge devices such as smartphones, wearables, and drones offers low latency, pervasive scalabilit

arXiv:2609.20677v1 Announce Type: new Abstract: The intersection of open-source processor architectures and machine learning is driving the demand for customizable, efficient, and accessible hardware.

arXiv:2609.18063v1 Announce Type: new Abstract: Mixture-of-experts (MoE) inference on consumer hardware is bounded by weight memory: a 35B-class model is 19.5GB at 4-bit, and sparsity shrinks the comp

arXiv:2609.17556v1 Announce Type: cross Abstract: Edge-deployed AI operate under dynamically changing power budgets, reliability requirements, and input distributions, requiring continuous adaptation.

arXiv:2609.17380v1 Announce Type: new Abstract: Open-source language models have a reproducibility problem. Despite releasing weights, training data, and recipes, none of them are provably reproducibl

arXiv:2609.15527v1 Announce Type: new Abstract: The energy efficiency of analog computing makes it one of the most promising candidates for deploying resource-intensive machine learning workloads on c

arXiv:2609.13290v1 Announce Type: new Abstract: Achieving biological-level running speeds has largely been pursued through advances in control algorithms, which improve the utilization of existing har

arXiv:2609.15367v1 Announce Type: new Abstract: Video Capsule Endoscopy (VCE) is a non-invasive medical examination that allows for the observation of the small intestine, which is otherwise difficult

arXiv:2609.15198v1 Announce Type: new Abstract: Contact-rich manipulation benefits from tactile feedback, yet physical tactile sensors introduce hardware, calibration, synchronization, and maintenance

arXiv:2609.12349v1 Announce Type: new Abstract: Socially assistive robots (SARs) can support structured health and well-being interventions, but hardware and cost constraints limit interaction complex

arXiv:2609.12920v1 Announce Type: new Abstract: We model total latency as the convolution of preprocessing, inference, and postprocessing distributions under a simplifying independence approximation,

arXiv:2609.11916v1 Announce Type: new Abstract: Camera traps often run in the field on edge hardware with limited or no connectivity, making small, locally-deployable vision-language models (VLMs) --

arXiv:2609.10105v1 Announce Type: cross Abstract: Compute governance today is a governance of training: the thresholds, reporting requirements, and frontier-AI regimes now in force attach to training

arXiv:2607.09835v2 Announce Type: replace Abstract: The recently introduced YOLO26 architecture incorporates NMS-free end-to-end inference and is optimized for deployment on resource-constrained CPU-b

arXiv:2609.11163v1 Announce Type: cross Abstract: Structured pruning of large language models (LLMs) offers hardware-efficient compression, yet existing methods require calibration data, gradient comp

arXiv:2609.10550v1 Announce Type: cross Abstract: AI deployment performance is shaped not by model architecture alone, but by interactions among compression, compiler transformations, and serving poli

arXiv:2609.11687v1 Announce Type: new Abstract: We revisit Kashin-decomposition-based weight quantization for large language models and propose an improved algorithm with stronger convergence properti

arXiv:2411.10406v4 Announce Type: replace-cross Abstract: In the span of four decades, quantum computation has evolved from an intellectual curiosity to a potentially realizable technology. Today, sma

arXiv:2609.07336v1 Announce Type: new Abstract: Morphology of the sub-basal nerve plexus (SNP) reflects peripheral nerve health, and corneal confocal microscopy (CCM) provides an important means for i

arXiv:2609.05522v1 Announce Type: new Abstract: Eye-tracking data are expensive to collect, requiring specialized hardware and controlled laboratory conditions, and difficult to share because of priva

arXiv:2609.07511v1 Announce Type: cross Abstract: Autonomous driving relies on High Definition (HD) maps for safe navigation. Traditional HD maps construction is costly in hardware, data and human res

arXiv:2609.06874v1 Announce Type: cross Abstract: High-resolution volumetric medical imaging is critical for clinical diagnosis, yet acquisition is often limited by scanner hardware, scan time, and fo

arXiv:2609.08659v1 Announce Type: new Abstract: Onboard object detection in Earth observation is constrained by limited computational resources and the absence of fully corrected imagery. While convol

arXiv:2609.05081v1 Announce Type: new Abstract: This paper introduces Deep Microcompression (DMC), a hardware-aware pipeline for deep learning inference on bare-metal microcontrollers. DMC integrates

arXiv:2609.05039v1 Announce Type: cross Abstract: Quantum software development is iterative and error-prone. Noisy hardware and repeated re-execution make experiment tracking, provenance, and reproduc

arXiv:2609.04912v1 Announce Type: cross Abstract: Reliability evaluation of deep neural networks under hardware faults commonly relies on fault injection, but exhaustive campaigns are intractable for

arXiv:2609.03276v1 Announce Type: new Abstract: Evaluating robot manipulation policies is becoming increasingly important as generalist models, particularly vision-language-action (VLA) models, are de

arXiv:2609.01775v1 Announce Type: cross Abstract: Automated repair of Hardware Description Language (HDL) designs remains challenging due to the large search space of candidate repairs and the strict

arXiv:2609.01743v1 Announce Type: new Abstract: Edge vision models are difficult to deploy on resource-constrained hardware, making low-bit post-training quantization (PTQ) attractive. In practice, st

arXiv:2609.01827v1 Announce Type: new Abstract: Structural magnetic resonance imaging (MRI) images are sometimes corrupted over a contiguous set of slices, where acquisition, motion, hardware, or reco

arXiv:2609.02546v1 Announce Type: new Abstract: Zero-shot generalization to unseen embodiments is important for generalizable vision-language-action (VLA) models as robot hardware evolves and task-spe

arXiv:2608.29667v1 Announce Type: new Abstract: The rapid development of LLMs incurs prohibitive memory footprints and intensive computational demands. Quantization-Aware Training (QAT) techniques hav

arXiv:2608.30792v1 Announce Type: cross Abstract: Obtaining data from neuromorphic sensors and processing it with Spiking Neural Networks is a promising solution to lower the energy cost of artificial

arXiv:2608.29601v1 Announce Type: cross Abstract: We present N_0-Foundation, a paradigm for tactile-enabled embodied manipulation, which integrates tactile sensing hardware, large-scale multimodal dat

arXiv:2608.28379v1 Announce Type: cross Abstract: Quantum federated learning enables collaborative model training across quantum devices without sharing raw data, and it faces the data and hardware he

arXiv:2608.27184v1 Announce Type: cross Abstract: Electronic design automation (EDA) has advanced engineering productivity through successive generations of tooling that progressively automate synthes

arXiv:2608.26800v1 Announce Type: new Abstract: We present an online learning framework that enables a bimanual robot to acquire diverse juggling patterns directly on physical hardware within minutes,

arXiv:2608.25539v1 Announce Type: new Abstract: A plant-health score can appear precise while resting on duplicated image families, a long-tailed label space, or a runtime file that was never evaluate

arXiv:2608.25178v1 Announce Type: new Abstract: Sidewalk-scale path extraction demands perception and planning that run reliably on compact, low-power hardware in cluttered, map-sparse environments. W

arXiv:2608.23816v1 Announce Type: new Abstract: Quantized fine-tuning (QLoRA) saves memory but not time. It dequantizes every 4-bit weight on the fly, so it trains more slowly than fp16 LoRA. We prese

arXiv:2608.21570v1 Announce Type: new Abstract: Deploying a safety layer for large language models on commodity hardware is constrained by the guards available to do it: current open guard models hold

arXiv:2608.21693v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) LLMs scale model capacity efficiently through sparse activation, but their large expert parameter footprint, routing imbalance,

arXiv:2608.21621v1 Announce Type: new Abstract: In on-device NLP tasks, limited resources of embedded hardware, such as the Raspberry Pi 5, require efficient inference strategies. This paper introduce

arXiv:2608.22048v1 Announce Type: new Abstract: Large language models are increasingly deployed on local hardware for privacy, cost, and accessibility reasons. Yet many evaluations emphasize accuracy