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

博客园 - 聂微东
爱范儿
爱范儿
博客园 - 三生石上(FineUI控件)
Recent Announcements
Recent Announcements
大猫的无限游戏
大猫的无限游戏
MongoDB | Blog
MongoDB | Blog
A
About on SuperTechFans
M
MIT News - Artificial intelligence
V
Visual Studio Blog
云风的 BLOG
云风的 BLOG
The GitHub Blog
The GitHub Blog
Jina AI
Jina AI
P
Proofpoint News Feed
博客园_首页
酷 壳 – CoolShell
酷 壳 – CoolShell
T
The Blog of Author Tim Ferriss
G
Google Developers Blog
宝玉的分享
宝玉的分享
Engineering at Meta
Engineering at Meta
Last Week in AI
Last Week in AI
aimingoo的专栏
aimingoo的专栏
罗磊的独立博客
N
Netflix TechBlog - Medium
人人都是产品经理
人人都是产品经理

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
Parallel decoder for Low Density Parity Check Codes: A MP...
Sudeep Kanur, Georgios Georgakarakos, Antti Siirilä, Jérémie Lag · 2022-11-26 · via cs.DC updates on arXiv.org

The near channel performance of Low Density Parity Check Codes (LDPC) has motivated its wide applications. Iterative decoding of LDPC codes provides significant implementation challenges as the complexity grows with the code size. Recent trends in integrating Multiprocessor System on Chip (MPSoC) with Network on Chip (NoC) gives a modular platform for parallel implementation. This paper presents an implementation platform for decoding LDPC codes based on HeMPS, an open source MPSoC framework based on NoC communication fabric. Reduced minimum sum algorithm is used for decoding LDPC codes and simulations are performed using HeMPS tool. The data rate and speedup factor measured for decoding a rate 1/2 LDPC code characterised by 252x504 parity matrix is presented