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

L
LangChain Blog
阮一峰的网络日志
阮一峰的网络日志
WordPress大学
WordPress大学
博客园 - 司徒正美
罗磊的独立博客
D
Docker
Last Week in AI
Last Week in AI
爱范儿
爱范儿
M
MIT News - Artificial intelligence
V
V2EX
Google DeepMind News
Google DeepMind News
小众软件
小众软件
Apple Machine Learning Research
Apple Machine Learning Research
Microsoft Security Blog
Microsoft Security Blog
T
Tailwind CSS Blog
MyScale Blog
MyScale Blog
V
Visual Studio Blog
博客园 - 叶小钗
B
Blog RSS Feed
A
About on SuperTechFans
F
Fortinet All Blogs
T
The Blog of Author Tim Ferriss
Martin Fowler
Martin Fowler
P
Proofpoint News Feed

cs.DC updates on arXiv.org

DUAL-BLADE: Dual-Path NVMe-Direct KV-Cache Offloading for Edge LLM Inference Progressive Semantic Communication for Efficient Edge-Cloud Vision-Language Models Efficient, VRAM-Constrained xLM Inference on Clients Folding Tensor and Sequence Parallelism for Memory-Efficient Transformer Training & Inference DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training AMMA: A Multi-Chiplet Memory-Centric Architecture for Low-Latency 1M Context Attention Serving RaMP: Runtime-Aware Megakernel Polymorphism for Mixture-of-Experts Spark Policy Toolkit: Semantic Contracts and Scalable Execution for Policy Learning in Spark Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directions PolyKV: A Shared Asymmetrically-Compressed KV Cache Pool for Multi-Agent LLM Inference A Survey on Split Learning for LLM Fine-Tuning: Models, Systems, and Privacy Optimizations ITAS: A Multi-Agent Architecture for LLM-Based Intelligent Tutoring Latency and Cost of Multi-Agent Intelligent Tutoring at Scale TACO: Efficient Communication Compression of Intermediate Tensors for Scalable Tensor-Parallel LLM Training FreeScale: Distributed Training for Sequence Recommendation Models with Minimal Scaling Cost CommFuse: Hiding Tail Latency via Communication Decomposition and Fusion for Distributed LLM Training A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning Usable Agent Discovery for Decentralized AI Systems Cloud to Edge: Benchmarking LLM Inference On Hardware-Accelerated Single-Board Computers Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Shard the Gradient, Scale the Model: Serverless Federated Aggregation via Gradient Partitioning Promoting Simple Agents: Ensemble Methods for Event-Log Prediction GraphLeap: Decoupling Graph Construction and Convolution for Vision GNN Acceleration on FPGA AGNT2: Autonomous Agent Economies on Interaction-Optimized Layer 2 Infrastructure FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels Stream-CQSA: Avoiding Out-of-Memory in Attention Computation via Flexible Workload Scheduling A Delta-Aware Orchestration Framework for Scalable Multi-Agent Edge Computing Federated Learning over Blockchain-Enabled Cloud Infrastructure Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not? Sherpa.ai Privacy-Preserving Multi-Party Entity Alignment without Intersection Disclosure for Noisy Identifiers
Self-Repairing Hardware Architecture for Safety-Critical ...
Shawkat Khairullah, Carl Elks · 2019-10-31 · via cs.DC updates on arXiv.org

Digital embedded systems in safety-critical cyber-physical-systems (CPSs) require high levels of resilience and robustness against different fault classes. In recent years, self-healing concepts based on biological physiology have received attention for the design and implementation of reliable systems. However, many of these approaches have not been architected from the outset with safety in mind, nor have they been targeted for the safety-related automation industry where the significant need exists. This study presents a new self-healing hardware architecture inspired by integrating biological concepts, fault tolerance techniques, and IEC 61131-3 operational schematics to facilitate adaption in automation and critical infrastructure. The proposed architecture is organised in two levels: the critical functions layer used for providing the intended service of the application and the healing layer that continuously monitors the correct execution of that application and generates health syndromes to heal any failure occurrence inside the functions layer. Finally, two industrial applications have been mapped on this architecture to date, and the authors believe the nexus of its concepts can positively impact the next generation of critical CPSs in industrial automation.