Tootfinder

Effizienzsteigerung: Wie Materialfehler die Chip-Technologie verändern
Forscher entdecken, dass gezielte Materialfehler die Energieeffizienz von Chips deutlich steigern können: Eine neue Perspektive für die Elektronikentwicklung.

Live-Webinar: Apple-Geräte effizient mit Microsoft Intune verwalten
Lernen Sie im Live-Webinar bewährte Methoden und praktikable Lösungen, um Apple-Geräte mit Intune zu managen.
https://www.…

Tu(r)ning AI Green: Exploring Energy Efficiency Cascading with Orthogonal Optimizations
Saurabhsingh Rajput, Mootez Saad, Tushar Sharma
https://arxiv.org/abs/2506.18289

Tu(r)ning AI Green: Exploring Energy Efficiency Cascading with Orthogonal Optimizations
AI's exponential growth intensifies computational demands and energy challenges. While practitioners employ various optimization techniques, that we refer as "knobs" in this paper, to tune model efficiency, these are typically afterthoughts and reactive ad-hoc changes applied in isolation without understanding their combinatorial effects on energy efficiency. This paper emphasizes on treating energy efficiency as the first-class citizen and as a fundamental design consideration for a compute-in…

FraPPE: Fast and Efficient Preference-based Pure Exploration
Udvas Das, Apurv Shukla, Debabrota Basu
https://arxiv.org/abs/2508.16487 https://arxiv.org/pdf…

FraPPE: Fast and Efficient Preference-based Pure Exploration
Preference-based Pure Exploration (PrePEx) aims to identify with a given confidence level the set of Pareto optimal arms in a vector-valued (aka multi-objective) bandit, where the reward vectors are ordered via a (given) preference cone $\mathcal{C}$. Though PrePEx and its variants are well-studied, there does not exist a computationally efficient algorithm that can optimally track the existing lower bound for arbitrary preference cones. We successfully fill this gap by efficiently solving the …

Decentralized Consensus Inference-based Hierarchical Reinforcement Learning for Multi-Constrained UAV Pursuit-Evasion Game
Xiang Yuming, Li Sizhao, Li Rongpeng, Zhao Zhifeng, Zhang Honggang
https://arxiv.org/abs/2506.18126

Decentralized Consensus Inference-based Hierarchical Reinforcement Learning for Multi-Constrained UAV Pursuit-Evasion Game
Multiple quadrotor unmanned aerial vehicle (UAV) systems have garnered widespread research interest and fostered tremendous interesting applications, especially in multi-constrained pursuit-evasion games (MC-PEG). The Cooperative Evasion and Formation Coverage (CEFC) task, where the UAV swarm aims to maximize formation coverage across multiple target zones while collaboratively evading predators, belongs to one of the most challenging issues in MC-PEG, especially under communication-limited con…

Jet-Nemotron: Efficient Language Model with Post Neural Architecture Search
Yuxian Gu, Qinghao Hu, Shang Yang, Haocheng Xi, Junyu Chen, Song Han, Han Cai
https://arxiv.org/abs/2508.15884

Jet-Nemotron: Efficient Language Model with Post Neural Architecture Search
We present Jet-Nemotron, a new family of hybrid-architecture language models, which matches or exceeds the accuracy of leading full-attention models while significantly improving generation throughput. Jet-Nemotron is developed using Post Neural Architecture Search (PostNAS), a novel neural architecture exploration pipeline that enables efficient model design. Unlike prior approaches, PostNAS begins with a pre-trained full-attention model and freezes its MLP weights, allowing efficient explorat…

Tri-Hybrid Beamforming for Radiation-Center Reconfigurable Antenna Array: Spectral Efficiency and Energy Efficiency
Yinchen Li, Chenhao Qi, Shiwen Mao, Octavia A. Dobre
https://arxiv.org/abs/2508.15924

Tri-Hybrid Beamforming for Radiation-Center Reconfigurable Antenna Array: Spectral Efficiency and Energy Efficiency
In this paper, we propose a tri-hybrid beamforming (THBF) architecture based on the radiation-center (RC) reconfigurable antenna array (RCRAA), including the digital beamforming, analog beamforming, and electromagnetic (EM) beamforming, where the EM beamformer design is modeled as RC selection. Aiming at spectral efficiency (SE) maximization subject to the hardware and power consumption constraints, we propose a tri-loop alternating optimization (TLAO) scheme for the THBF design, where the digi…

Self-Disguise Attack: Induce the LLM to disguise itself for AIGT detection evasion
Yinghan Zhou, Juan Wen, Wanli Peng, Zhengxian Wu, Ziwei Zhang, Yiming Xue
https://arxiv.org/abs/2508.15848

Self-Disguise Attack: Induce the LLM to disguise itself for AIGT detection evasion
AI-generated text (AIGT) detection evasion aims to reduce the detection probability of AIGT, helping to identify weaknesses in detectors and enhance their effectiveness and reliability in practical applications. Although existing evasion methods perform well, they suffer from high computational costs and text quality degradation. To address these challenges, we propose Self-Disguise Attack (SDA), a novel approach that enables Large Language Models (LLM) to actively disguise its output, reducing…

MEDEA: A Design-Time Multi-Objective Manager for Energy-Efficient DNN Inference on Heterogeneous Ultra-Low Power Platforms
Hossein Taji, Jos\'e Miranda, Miguel Pe\'on-Quir\'os, David Atienza
https://arxiv.org/abs/2506.19067

MEDEA: A Design-Time Multi-Objective Manager for Energy-Efficient DNN Inference on Heterogeneous Ultra-Low Power Platforms
The growing demand for on-device AI necessitates energy-efficient execution of DNN based applications on resource-constrained ultra-low power (ULP) platforms. Heterogeneous architectures, combining specialized processing elements (PEs), have emerged as a key solution for achieving the required performance and energy efficiency. However, optimizing energy while executing applications on these platforms requires efficiently managing platform resources like PEs, power features, and memory footprin…

Annif at the GermEval-2025 LLMs4Subjects Task: Traditional XMTC Augmented by Efficient LLMs
Osma Suominen, Juho Inkinen, Mona Lehtinen
https://arxiv.org/abs/2508.15877 https://

Annif at the GermEval-2025 LLMs4Subjects Task: Traditional XMTC Augmented by Efficient LLMs
This paper presents the Annif system in the LLMs4Subjects shared task (Subtask 2) at GermEval-2025. The task required creating subject predictions for bibliographic records using large language models, with a special focus on computational efficiency. Our system, based on the Annif automated subject indexing toolkit, refines our previous system from the first LLMs4Subjects shared task, which produced excellent results. We further improved the system by using many small and efficient language mo…