Search: “hardware”

CARMEN: CORDIC-Accelerated Resource-Efficient Multi-Precision Inference Engine for Deep Learning

arXiv:2605.06878v1 Announce Type: cross Abstract: This paper presents CARMEN, a runtime-adaptive, CORDIC-accelerated multi-precision vector engine for resource-efficient deep learning inference. The k

researcharxiv-cs-ro

11 May 2026

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Online Continual Learning on Intel Loihi 2 via a Co-designed Spiking Neural Network

DGX agent

arXiv:2511.01553v2 Announce Type: replace Abstract: AI systems on edge devices require online continual learning -- adapting to non-stationary streams and unfamiliar classes without catastrophic forge

local-aiarxiv-cs-lg

7 May 2026

Personalized Spiking Neural Networks with Ferroelectric Synapses for EEG Signal Processing

arXiv:2601.00020v3 Announce Type: replace-cross Abstract: Electroencephalography (EEG)-based brain-computer interfaces (BCIs) are strongly affected by non-stationary neural signals that vary across se

7 May 2026

Benchmarking Local Language Models for Social Robots using Edge Devices

arXiv:2605.03111v1 Announce Type: cross Abstract: Social-educational robots designed for socially interactive pedagogical support, such as the Robot Study Companion (RSC), rely on responsive, privacy-

local-aiarxiv-cs-cl

Jiao: Bridging Isolation and Customization in Mixed Criticality Robotics

model-releasesarxiv-cs-ro

arXiv:2605.03641v1 Announce Type: new Abstract: Consumer robotics demands consolidation of safety-critical control, perception pipelines, and user applications on shared multicore platforms. While sta

Learning Reactive Dexterous Grasping via Hierarchical Task-Space RL Planning and Joint-Space QP Control

arXiv:2605.03363v1 Announce Type: new Abstract: In this work, we propose a hybrid hierarchical control framework for reactive dexterous grasping that explicitly decouples high-level spatial intent fro

Real Image Denoising with Knowledge Distillation for High-Performance Mobile NPUs

arXiv:2605.03680v1 Announce Type: new Abstract: While deep-learning-based image restoration has achieved unprecedented fidelity, deployment on mobile Neural Processing Units (NPUs) remains bottlenecke

A Unified Multi-Dynamics Framework for Perception-Oriented Modeling in Tendon-Driven Continuum Robots

arXiv:2511.18088v2 Announce Type: replace Abstract: Tendon-driven continuum robots offer intrinsically safe and contact-rich interactions owing to their kinematic redundancy and structural compliance.

Edge-Efficient Image Restoration: Transformer Distillation into State-Space Models

arXiv:2605.02794v1 Announce Type: new Abstract: We propose a modular framework for hybrid image restoration that integrates transformer and state-space model (SSM) blocks with a focus on improving run

researcharxiv-cs-cv

Low-rank surrogate modeling and stochastic zero-order optimization for training of neural networks with black-box layers

arXiv:2509.15113v2 Announce Type: replace Abstract: The growing demand for energy-efficient, high-performance AI systems has led to increased attention on alternative computing platforms (e.g., photon

Silicon Showdown: Performance, Efficiency, and Ecosystem Barriers in Consumer-Grade LLM Inference

arXiv:2605.00519v2 Announce Type: cross Abstract: The operational landscape of local Large Language Model (LLM) inference has shifted from lightweight models to datacenter-class weights exceeding 70B

VILAS: A VLA-Integrated Low-cost Architecture with Soft Grasping for Robotic Manipulation

model-releasesarxiv-cs-ro

arXiv:2605.02037v1 Announce Type: new Abstract: We present VILAS, a fully low-cost, modular robotic manipulation platform designed to support end-to-end vision-language-action (VLA) policy learning an

RadLite: Multi-Task LoRA Fine-Tuning of Small Language Models for CPU-Deployable Radiology AI

arXiv:2605.00421v1 Announce Type: new Abstract: Large language models (LLMs) show promise in radiology but their deployment is limited by computational requirements that preclude use in resource-const

local-aiarxiv-cs-cl

4 May 2026

LUNA: LUT-Based Neural Architecture for Fast and Low-Cost Qubit Readout

arXiv:2512.07808v2 Announce Type: replace-cross Abstract: Qubit readout is a critical operation in quantum computing systems, which maps the analog response of qubits into discrete classical states. D

1 May 2026

Hierarchical adaptive control for real-time dynamic inference at the edge

arXiv:2604.26470v1 Announce Type: new Abstract: Industrial systems increasingly depend on Machine Learning (ML), and operate on heterogeneous nodes that must satisfy tight latency, energy, and memory

30 Apr 2026

Lightweight Real-Time Rendering Parameter Optimization via XGBoost-Driven Lookup Tables

arXiv:2604.25178v1 Announce Type: new Abstract: Achieving a desirable balance between rendering quality and real-time performance is a long-standing challenge in modern game and rendering engines, par

29 Apr 2026

Breaking the Resource Wall: Geometry-Guided Sequence Modeling for Efficient Semantic Segmentation

arXiv:2604.23399v1 Announce Type: new Abstract: High-performance semantic segmentation has achieved significant progress in recent years, often driven by increasingly large backbones and higher comput

local-aiarxiv-cs-cv

ELSA: Exact Linear-Scan Attention for Fast and Memory-Light Vision Transformers

arXiv:2604.23798v1 Announce Type: cross Abstract: Existing attention accelerators often trade exact softmax semantics, depend on fused Tensor Core kernels, or incur sequential depth that limits FP32 t

Fixed-Reservoir vs Variational Quantum Architectures for Chaotic Dynamics: Benchmarking QRC and QPINN on the Lorenz System

arXiv:2604.23743v1 Announce Type: cross Abstract: Deploying quantum machine learning on NISQ devices requires architectures where training overhead does not negate computational advantages. We systema

Kwai Summary Attention Technical Report

arXiv:2604.24432v1 Announce Type: cross Abstract: Long-context ability, has become one of the most important iteration direction of next-generation Large Language Models, particularly in semantic unde

local-aiarxiv-cs-ai

On the Convergence Theory of Pipeline Gradient-based Analog In-memory Training

arXiv:2410.15155v3 Announce Type: replace Abstract: Aiming to accelerate the training of large deep neural networks (DNN) in an energy-efficient way, analog in-memory computing (AIMC) emerges as a sol

Partition-of-Unity Gaussian Kolmogorov-Arnold Networks

arXiv:2604.23599v1 Announce Type: cross Abstract: Gaussian basis functions provide an efficient and flexible alternative to spline activations in KANs. In this work, we introduce the partition-of-unit

QuietWalk: Physics-Informed Reinforcement Learning for Ground Reaction Force-Aware Humanoid Locomotion Under Diverse Footwear

arXiv:2604.23702v1 Announce Type: new Abstract: Humanoid robots operating in human-centered environments (e.g., homes, hospitals, and offices) must mitigate foot--ground impact transients, as impact-i

RefEvo: Agentic Design with Co-Evolutionary Verification for Agile Reference Model Generation

arXiv:2604.24218v1 Announce Type: cross Abstract: As the complexity of System-on-Chip (SoC) designs grows, the shift-left paradigm necessitates the rapid development of high-fidelity reference models

A systematic review of generative AI usage for IT project management

arXiv:2604.21958v1 Announce Type: cross Abstract: This paper aims to synthesize current knowledge on generative AI in IT project management using the PRISMA methodology to provide researchers with a c

Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond

arXiv:2604.22748v1 Announce Type: new Abstract: As AI systems move from generating text to accomplishing goals through sustained interaction, the ability to model environment dynamics becomes a centra

local-aiarxiv-cs-ai

Foundation models for discovering robust biomarkers of neurological disorders from dynamic functional connectivity

arXiv:2604.22018v1 Announce Type: cross Abstract: Several brain foundation models (FM) have recently been proposed to predict brain disorders by modelling dynamic functional connectivity (FC). While t

Applications

Report for NSF Workshop on AI for Electronic Design Automation

arXiv:2601.14541v4 Announce Type: replace-cross Abstract: This report distills the discussions and recommendations from the NSF Workshop on AI for Electronic Design Automation (EDA), held on December

applicationsarxiv-cs-ai

DAVIS: OOD Detection via Dominant Activations and Variance for Increased Separation

arXiv:2601.22703v2 Announce Type: replace Abstract: Detecting out-of-distribution (OOD) inputs is a critical safeguard for deploying machine learning models in the real world. However, most post-hoc d

24 Apr 2026

NPU Design for Diffusion Language Model Inference

arXiv:2601.20706v2 Announce Type: replace-cross Abstract: Diffusion-based LLMs (dLLMs) fundamentally depart from traditional autoregressive (AR) LLM inference: they leverage bidirectional attention, b

24 Apr 2026

Applications

Toward a Multi-Layer ML-Based Security Framework for Industrial IoT

arXiv:2603.24111v3 Announce Type: replace-cross Abstract: The Industrial Internet of Things (IIoT) introduces significant security challenges as resource-constrained devices become increasingly integr

applicationsarxiv-cs-lg

24 Apr 2026

Accelerating PayPal's Commerce Agent with Speculative Decoding: An Empirical Study on EAGLE3 with Fine-Tuned Nemotron Models

arXiv:2604.19767v1 Announce Type: cross Abstract: We evaluate speculative decoding with EAGLE3 as an inference-time optimization for PayPal's Commerce Agent, powered by a fine-tuned llama3.1-nemotron-

23 Apr 2026

From Signal Degradation to Computation Collapse: Uncovering the Two Failure Modes of LLM Quantization

arXiv:2604.19884v1 Announce Type: cross Abstract: Post-Training Quantization (PTQ) is critical for the efficient deployment of Large Language Models (LLMs). While 4-bit quantization is widely regarded

23 Apr 2026

Task-Stratified Knowledge Scaling Laws for Post-Training Quantized Large Language Models

arXiv:2508.18609v4 Announce Type: replace-cross Abstract: Post-Training Quantization (PTQ) is a critical strategy for efficient Large Language Models (LLMs) deployment. However, existing scaling laws

23 Apr 2026

Are Large Language Models Economically Viable for Industry Deployment?

model-releasesarxiv-cs-cl

arXiv:2604.19342v1 Announce Type: new Abstract: Generative AI-powered by Large Language Models (LLMs)-is increasingly deployed in industry across healthcare decision support, financial analytics, ente

Comparing energy consumption and accuracy in text classification inference

arXiv:2508.14170v2 Announce Type: replace Abstract: The increasing deployment of large language models (LLMs) in natural language processing (NLP) tasks raises concerns about energy efficiency and sus

researcharxiv-cs-cl

Applications

Enabling Vibration-Based Gesture Recognition on Everyday Furniture via Energy-Efficient FPGA Implementation of 1D Convolutional Networks

arXiv:2510.23156v2 Announce Type: replace-cross Abstract: The growing demand for smart home interfaces has increased interest in non-intrusive sensing methods like vibration-based gesture recognition.

applicationsarxiv-cs-ai

StrikeWatch: Wrist-worn Gait Recognition with Compact Time-series Models on Low-power FPGAs

arXiv:2510.24738v2 Announce Type: replace-cross Abstract: Running offers substantial health benefits, but improper gait patterns can lead to injuries, particularly without expert feedback. While prior

Thermal Anomaly Detection using Physics Aware Neuromorphic Networks: Comparison between Raw and L1C Sentinel-2 Data

arXiv:2604.18606v1 Announce Type: cross Abstract: Damage caused by bushfires and volcanic eruptions escalates rapidly when detection is delayed, making fast and reliable early warning capabilities ess

A Survey of Spatial Memory Representations for Efficient Robot Navigation

arXiv:2604.16482v1 Announce Type: new Abstract: As vision-based robots navigate larger environments, their spatial memory grows without bound, eventually exhausting computational resources, particular

Chasing Ghosts: A Simulation-to-Real Olfactory Navigation Stack with Optional Vision Augmentation

arXiv:2602.19577v2 Announce Type: replace Abstract: Autonomous odor source localization remains a challenging problem for aerial robots due to turbulent airflow, sparse and delayed sensory signals, an

FlashFPS: Efficient Farthest Point Sampling for Large-Scale Point Clouds via Pruning and Caching

arXiv:2604.17720v1 Announce Type: cross Abstract: Point-based Neural Networks (PNNs) have become a key approach for point cloud processing. However, a core operation in these models, Farthest Point Sa

FrameDiT: Diffusion Transformer with Matrix Attention for Efficient Video Generation

arXiv:2603.09721v2 Announce Type: replace Abstract: High-fidelity video generation remains challenging for diffusion models due to the difficulty of modeling complex spatio-temporal dynamics efficient

researcharxiv-cs-cv

From 2:4 to 8:16 sparsity patterns in LLMs for Outliers and Weights with Variance Correction

arXiv:2507.03052v2 Announce Type: replace Abstract: As large language models (LLMs) grow in size, efficient compression techniques like quantization and sparsification are critical. While quantization

Tutorials

MM-Hand: A 21-DOF Multi-modal Modular Dexterous Robotic Hand with Remote Actuation

arXiv:2604.17245v1 Announce Type: new Abstract: High-DOF dexterous hands require compact actuation, rich sensing, and reliable thermal behavior, but conventional designs often occupy valuable in-hand

tutorialsarxiv-cs-ro

ProTrain: Efficient LLM Training via Memory-Aware Techniques

model-releasesarxiv-cs-lg

arXiv:2406.08334v2 Announce Type: replace-cross Abstract: Memory pressure has emerged as a dominant constraint in scaling the training of large language models (LLMs), particularly in resource-constra

AscendKernelGen: A Systematic Study of LLM-Based Kernel Generation for Neural Processing Units

arXiv:2601.07160v2 Announce Type: replace Abstract: To meet the ever-increasing demand for computational efficiency, Neural Processing Units (NPUs) have become critical in modern AI infrastructure. Ho

20 Apr 2026

Sequential KV Cache Compression via Probabilistic Language Tries: Beyond the Per-Vector Shannon Limit

arXiv:2604.15356v1 Announce Type: cross Abstract: Recent work on KV cache quantization, culminating in TurboQuant, has approached the Shannon entropy limit for per-vector compression of transformer ke