Tootfinder

Autonomous Navigation of Cloud-Controlled Quadcopters in Confined Spaces Using Multi-Modal Perception and LLM-Driven High Semantic Reasoning
Shoaib Ahmmad, Zubayer Ahmed Aditto, Md Mehrab Hossain, Noushin Yeasmin, Shorower Hossain
https://arxiv.org/abs/2508.07885

Autonomous Navigation of Cloud-Controlled Quadcopters in Confined Spaces Using Multi-Modal Perception and LLM-Driven High Semantic Reasoning
This paper introduces an advanced AI-driven perception system for autonomous quadcopter navigation in GPS-denied indoor environments. The proposed framework leverages cloud computing to offload computationally intensive tasks and incorporates a custom-designed printed circuit board (PCB) for efficient sensor data acquisition, enabling robust navigation in confined spaces. The system integrates YOLOv11 for object detection, Depth Anything V2 for monocular depth estimation, a PCB equipped with Ti…

SSD Offloading for LLM Mixture-of-Experts Weights Considered Harmful in Energy Efficiency
Kwanhee Kyung, Sungmin Yun, Jung Ho Ahn
https://arxiv.org/abs/2508.06978 https://

SSD Offloading for LLM Mixture-of-Experts Weights Considered Harmful in Energy Efficiency
Large Language Models (LLMs) applying Mixture-of-Experts (MoE) scale to trillions of parameters but require vast memory, motivating a line of research to offload expert weights from fast-but-small DRAM (HBM) to denser Flash SSDs. While SSDs provide cost-effective capacity, their read energy per bit is substantially higher than that of DRAM. This paper quantitatively analyzes the energy implications of offloading MoE expert weights to SSDs during the critical decode stage of LLM inference. Our a…

Improving compiler support for SIMD offload using Arm Streaming SVE
Mohamed Husain Noor Mohamed, Adarsh Patil, Latchesar Ionkov, Eric Van Hensbergen
https://arxiv.org/abs/2506.02233

Improving compiler support for SIMD offload using Arm Streaming SVE
The wider adoption of tightly coupled core-adjacent accelerators, such as Arm Scalable Matrix Extension (SME), hinges on lowering software programming complexity. In this paper, we focus on enabling the use of SME architecture in Streaming Scalable Vector Extension (SSVE) mode for workloads written in C/C++. While current compilers optimize loops for all types of SIMD instructions, these techniques primarily target vector units within the core and falter when applied to disaggregated, core-adja…

An Overlapping Coalition Game Approach for Collaborative Block Mining and Edge Task Offloading in MEC-assisted Blockchain Networks
Licheng Ye, Zehui Xiong, Lin Gao, Dusit Niyato
https://arxiv.org/abs/2508.06031

An Overlapping Coalition Game Approach for Collaborative Block Mining and Edge Task Offloading in MEC-assisted Blockchain Networks
Mobile edge computing (MEC) is a promising technology that enhances the efficiency of mobile blockchain networks, by enabling miners, often acted by mobile users (MUs) with limited computing resources, to offload resource-intensive mining tasks to nearby edge computing servers. Collaborative block mining can further boost mining efficiency by allowing multiple miners to form coalitions, pooling their computing resources and transaction data together to mine new blocks collaboratively. Therefore…

Replaced article(s) found for cs.DC. https://arxiv.org/list/cs.DC/new
[1/1]:
- To Offload or Not To Offload: Model-driven Comparison of Edge-native and On-device Processing
Nathan Ng, David Irwin, Ananthram Swami, Don Towsley, Prashant Shenoy

In-Network Memory Access: Bridging SmartNIC and Host Memory
Mohammed Zain Farooqi, Masoud Hemmatpour, Tore Heide Larsen
https://arxiv.org/abs/2507.04001 ht…

In-Network Memory Access: Bridging SmartNIC and Host Memory
SmartNICs have been increasingly utilized across various applications to offload specific computational tasks, thereby enhancing overall system performance. However, this offloading process introduces several communication challenges that must be addressed for effective integration. A key challenge lies in establishing efficient communication between the offloaded components and the main application running on the host. In this study, we evaluate different approaches for achieving memory access…

From Patient Burdens to User Agency: Designing for Real-Time Protection Support in Online Health Consultations
Shuning Zhang, Ying Ma, Yongquan `Owen' Hu, Ting Dang, Hong Jia, Xin Yi, Hewu Li
https://arxiv.org/abs/2508.00328

From Patient Burdens to User Agency: Designing for Real-Time Protection Support in Online Health Consultations
Online medical consultation platforms, while convenient, are undermined by significant privacy risks that erode user trust. We first conducted in-depth semi-structured interviews with 12 users to understand their perceptions of security and privacy landscapes on online medical consultation platforms, as well as their practices, challenges and expectation. Our analysis reveals a critical disconnect between users' desires for anonymity and control, and platform realities that offload the responsi…

A Novel Deep Reinforcement Learning Method for Computation Offloading in Multi-User Mobile Edge Computing with Decentralization
Nguyen Chi Long, Trinh Van Chien, Ta Hai Tung, Van Son Nguyen, Trong-Minh Hoang, Nguyen Ngoc Hai Dang
https://arxiv.org/abs/2506.02458

A Novel Deep Reinforcement Learning Method for Computation Offloading in Multi-User Mobile Edge Computing with Decentralization
Mobile edge computing (MEC) allows appliances to offload workloads to neighboring MEC servers that have the potential for computation-intensive tasks with limited computational capabilities. This paper studied how deep reinforcement learning (DRL) algorithms are used in an MEC system to find feasible decentralized dynamic computation offloading strategies, which leads to the construction of an extensible MEC system that operates effectively with finite feedback. Even though the Deep Determinist…

LCS: An AI-based Low-Complexity Scaler for Power-Efficient Super-Resolution of Game Content
Simon Pochinda, Momen K. Tageldeen, Mark Thompson, Tony Rinaldi, Troy Giorshev, Keith Lee, Jie Zhou, Frederick Walls
https://arxiv.org/abs/2507.22873

LCS: An AI-based Low-Complexity Scaler for Power-Efficient Super-Resolution of Game Content
The increasing complexity of content rendering in modern games has led to a problematic growth in the workload of the GPU. In this paper, we propose an AI-based low-complexity scaler (LCS) inspired by state-of-the-art efficient super-resolution (ESR) models which could offload the workload on the GPU to a low-power device such as a neural processing unit (NPU). The LCS is trained on GameIR image pairs natively rendered at low and high resolution. We utilize adversarial training to encourage rec…

This https://arxiv.org/abs/2506.02774 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGR_…

Voyager: Real-Time Splatting City-Scale 3D Gaussians on Your Phone
3D Gaussian Splatting (3DGS) is an emerging technique for photorealistic 3D scene rendering. However, rendering city-scale 3DGS scenes on mobile devices, e.g., your smartphones, remains a significant challenge due to the limited resources on mobile devices. A natural solution is to offload computation to the cloud; however, naively streaming rendered frames from the cloud to the client introduces high latency and requires bandwidth far beyond the capacity of current wireless networks. In this…

QTCAJOSA: Low-Complexity Joint Offloading and Subchannel Allocation for NTN-Enabled IoMT
Alejandro Flores C., Konstantinos Ntontin, Ashok Bandi, Symeon Chatzinotas
https://arxiv.org/abs/2507.13242

QTCAJOSA: Low-Complexity Joint Offloading and Subchannel Allocation for NTN-Enabled IoMT
In this work, we consider the resource allocation problem for task offloading from Internet of Medical Things (IoMT) devices, to a non-terrestrial network. The architecture considers clusters of IoMT devices that offload their tasks to a dedicated unmanned aerial vehicle (UAV) serving as a multi-access edge computing (MEC) server, which can compute the task or further offload it to an available high-altitude platform station (HAPS) or to a low-earth orbit (LEO) satellite for remote computing. W…

I think maybe it isn't #mastodon that is slowing down and filling up #footiMac.
It's the 700GB of webcam images/videos.
Just bought a fast dual USB A/C 500GB flash stick to offload this stuff and lighten things up a bit.
This is stuff I need to archive anyway. I want to gradually build a physical USB stick based digital library of webcam imagery from the cams @….
#mastoadmin #weather #webcams

Knowledge-Guided Attention-Inspired Learning for Task Offloading in Vehicle Edge Computing
Ke Ma, Junfei Xie
https://arxiv.org/abs/2506.04456 https://

Knowledge-Guided Attention-Inspired Learning for Task Offloading in Vehicle Edge Computing
Vehicle edge computing (VEC) brings abundant computing resources close to vehicles by deploying them at roadside units (RSUs) or base stations, thereby enabling diverse computation-intensive and delay sensitive applications. Existing task offloading strategies are often computationally expensive to execute or generate suboptimal solutions. In this paper, we propose a novel learning-based approach, Knowledge-guided Attention-inspired Task Offloading (KATO), designed to efficiently offload tasks …

PUL: Pre-load in Software for Caches Wouldn't Always Play Along
Arthur Bernhardt, Sajjad Tamimi, Florian Stock, Andreas Koch, Ilia Petrov
https://arxiv.org/abs/2506.16976

PUL: Pre-load in Software for Caches Wouldn't Always Play Along
Memory latencies and bandwidth are major factors, limiting system performance and scalability. Modern CPUs aim at hiding latencies by employing large caches, out-of-order execution, or complex hardware prefetchers. However, software-based prefetching exhibits higher efficiency, improving with newer CPU generations. In this paper we investigate software-based, post-Moore systems that offload operations to intelligent memories. We show that software-based prefetching has even higher potential i…

Latency Minimization Oriented Radio and Computation Resource Allocations for 6G V2X Networks with ISCC
Peng Liu, Xinyi Wang, Zesong Fei, Yuan Wu, Jie Xu, Arumugam Nallanathan
https://arxiv.org/abs/2507.16375

Latency Minimization Oriented Radio and Computation Resource Allocations for 6G V2X Networks with ISCC
Incorporating mobile edge computing (MEC) and integrated sensing and communication (ISAC) has emerged as a promising technology to enable integrated sensing, communication, and computing (ISCC) in the sixth generation (6G) networks. ISCC is particularly attractive for vehicle-to-everything (V2X) applications, where vehicles perform ISAC to sense the environment and simultaneously offload the sensing data to roadside base stations (BSs) for remote processing. In this paper, we investigate a part…

Voyager: Real-Time Splatting City-Scale 3D Gaussians on Your Phone
Zheng Liu, He Zhu, Xinyang Li, Yirun Wang, Yujiao Shi, Wei Li, Jingwen Leng, Minyi Guo, Yu Feng
https://arxiv.org/abs/2506.02774

Voyager: Real-Time Splatting City-Scale 3D Gaussians on Your Phone
3D Gaussian Splatting (3DGS) is an emerging technique for photorealistic 3D scene rendering. However, rendering city-scale 3DGS scenes on mobile devices, e.g., your smartphones, remains a significant challenge due to the limited resources on mobile devices. A natural solution is to offload computation to the cloud; however, naively streaming rendered frames from the cloud to the client introduces high latency and requires bandwidth far beyond the capacity of current wireless networks. In this…

Reconfigurable Intelligent Surface-Enabled Green and Secure Offloading for Mobile Edge Computing Networks
Tong-Xing Zheng, Xinji Wang, Xin Chen, Di Mao, Jia Shi, Cunhua Pan, Chongwen Huang, Haiyang Ding, Zan Li
https://arxiv.org/abs/2507.16666

Reconfigurable Intelligent Surface-Enabled Green and Secure Offloading for Mobile Edge Computing Networks
This paper investigates a multi-user uplink mobile edge computing (MEC) network, where the users offload partial tasks securely to an access point under the non-orthogonal multiple access policy with the aid of a reconfigurable intelligent surface (RIS) against a multi-antenna eavesdropper. We formulate a non-convex optimization problem of minimizing the total energy consumption subject to secure offloading requirement, and we build an efficient block coordinate descent framework to iteratively…

"BP agrees to sell US onshore wind business as it shifts back to oil"
#BP #BritishPetroleum #FossilFuels #Energy

On secure UAV-aided ISCC systems
Hongjiang Lei, Congke Jiang, Ki-Hong Park, Mohamed A. Aboulhassan, Sen Zhou, Gaofeng Pan
https://arxiv.org/abs/2506.13137 …

On secure UAV-aided ISCC systems
Integrated communication and sensing, which can make full use of the limited spectrum resources to perform communication and sensing tasks simultaneously, is an up-and-coming technology in wireless communication networks. In this work, we investigate the secrecy performance of an uncrewed aerial vehicle (UAV)-assisted secure integrated communication, sensing, and computing system, where the UAV sends radar signals to locate and disrupt potential eavesdroppers while providing offload services to…