惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

Stack Overflow Blog
Stack Overflow Blog
云风的 BLOG
云风的 BLOG
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
Recent Announcements
Recent Announcements
Microsoft Security Blog
Microsoft Security Blog
Microsoft Azure Blog
Microsoft Azure Blog
J
Java Code Geeks
D
DataBreaches.Net
U
Unit 42
P
Proofpoint News Feed
I
InfoQ
Apple Machine Learning Research
Apple Machine Learning Research
Google DeepMind News
Google DeepMind News
博客园 - Franky
博客园_首页
IT之家
IT之家
博客园 - 叶小钗
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
酷 壳 – CoolShell
酷 壳 – CoolShell
博客园 - 【当耐特】
Hugging Face - Blog
Hugging Face - Blog
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
阮一峰的网络日志
阮一峰的网络日志

eess.SP updates on arXiv.org

ECG-biometrics-bench: A Unified Framework for Reproducible Benchmarking of ECG Biometrics Physiology-Aware Masked Cross-Modal Reconstruction for Biosignal Representation Learning Towards Improving Speaker Distance Estimation through Generative Impulse Response Augmentation Federated Learning with Hypergradient-based Online Update of Aggregation Weights Soft Graph Diffusion Transformer for MIMO Detection SPLICE: Latent Diffusion over JEPA Embeddings for Conformal Time-Series Inpainting Sequential Inference for Gaussian Processes: A Signal Processing Perspective Statistical Channel Fingerprint Construction for Massive MIMO: A Unified Tensor Learning Framework Recent Advances in mm-Wave and Sub-THz/THz Oscillators for FutureG Technologies Cross-Subject Generalization for EEG Decoding: A Survey of Deep Learning Methods Super-resolution Multi-signal Direction-of-Arrival Estimation by Hankel-structured Sensing and Decomposition Hankel and Toeplitz Rank-1 Decomposition of Arbitrary Matrices with Applications to Signal Direction-of-Arrival Estimation Adaptive Transform Coding for Semantic Compression EdgeSpike: Spiking Neural Networks for Low-Power Autonomous Sensing in Edge IoT Architectures Sparse Graph Learning from Sparse Data via Fiedler Number Maximization A Deep Learning Model for Battery State Prediction towards Intelligent Energy Management Transfer Learning for Tonal Noise Prediction in VRF Units Using Thermodynamic and Vibration Signals EVT-Based Generative AI for Tail-Aware Channel Estimation Monitoring exposure-length variations in submarine power cables using distributed fiber-optic sensing BandRouteNet: An Adaptive Band Routing Neural Network for EEG Artifact Removal Phase-Separated Complex Hilbert PCA on Markerless 3D Pose Estimation Data: A Global Phase Network and Its Extension to a Continuous Field on the Body Surface Selective Correlation Based Knowledge Distillation for Ground Reaction Force Estimation Deep Learning-Enabled Dissolved Oxygen Sensing in Biofouling Environments for Ocean Monitoring Speech Enhancement Based on Drifting Models Robust and Clinically Reliable EEG Biomarkers: A Cross Population Framework for Generalizable Parkinson's Disease Detection An AI-Based Supervisory Measurement Integrity Validation Layer for Cyber-Resilient AC/DC Protection in Inverter-Based Microgrids Explainable AI in Speaker Recognition -- Making Latent Representations Understandable Time-Localized Parametric Decomposition of Respiratory Airflow for Sub-Breath Analysis NAKUL-Med: Spectral-Graph State Space Models with Dynamics Kernels for Medical Signals An Algorithm for On-Sensor Agnostic Detection of Changes in Human Activity for Ultra-Low-Power Applications
Simulation Environment for Link Energy Estimation in Netw...
Jan Moritz Joseph, Lennart Bamberg, Imad Hajjar, Robert Schmidt, · 2019-05-23 · via eess.SP updates on arXiv.org

Network-on-chip (NoC) is the most promising design paradigm for the interconnect architecture of a multiprocessor system-on-chip (MPSoC). On the downside, a NoC has a significant impact on the overall energy consumption of the system. NoC simulators are highly relevant for design space exploration even at an early stage. Since links in NoC consume up to 50% of the energy, a realistic energy consumption of links in NoC simulators is important. This work presents a simulation environment which implements a technique to precisely estimate the data dependent link energy consumption in NoCs with virtual channels for the first time. Our model works at a high level of abstraction, making it feasible to estimate the energy requirements at an early design stage. Additionally, it enables the fast evaluation and early exploration of low-power coding techniques. The presented model is applicable for 2D and 3D NoCs. A case study for an image processing application shows that the current link model leads to an underestimate of the link energy consumption by up to a factor of four. In contrast, the technique presented in this paper estimates the energy quantities precisely with an error below 1% compared to results obtained by precise, but computational extensive, bit-level simulation.