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

WordPress大学
WordPress大学
J
Java Code Geeks
Martin Fowler
Martin Fowler
Microsoft Azure Blog
Microsoft Azure Blog
月光博客
月光博客
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
人人都是产品经理
人人都是产品经理
有赞技术团队
有赞技术团队
爱范儿
爱范儿
Engineering at Meta
Engineering at Meta
GbyAI
GbyAI
博客园 - 【当耐特】
Y
Y Combinator Blog
Last Week in AI
Last Week in AI
MongoDB | Blog
MongoDB | Blog
G
Google Developers Blog
博客园 - 三生石上(FineUI控件)
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
大猫的无限游戏
大猫的无限游戏
罗磊的独立博客
The Cloudflare Blog
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
V
V2EX
博客园 - 司徒正美

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
Meta-level issues in Offloading: Scoping, Composition, De...
André DeHon, Hans Giesen, Nik Sultana, Yuanlong Xiao · 2021-04-05 · via cs.DC updates on arXiv.org

This paper argues for an accelerator development toolchain that takes into account the whole system containing the accelerator. With whole-system visibility, the toolchain can better assist accelerator scoping and composition in the context of the expected workloads and intended performance objectives. Despite being focused on the 'meta-level' of accelerators, this would build on existing and ongoing DSLs and toolchains for accelerator design. Basing this on our experience in programmable networking and reconfigurable-hardware programming, we propose an integrative approach that relies on three activities: (i) generalizing the focus of acceleration to offloading to accommodate a broader variety of non-functional needs -- such as security and power use -- while using similar implementation approaches, (ii) discovering what to offload, and to what hardware, through semi-automated analysis of a whole system that might compose different offload choices that changeover time, (iii) connecting with research and state-of-the-art approaches for using domain-specific languages (DSLs) and high-level synthesis (HLS) systems for custom offload development. We outline how this integration can drive new development tooling that accepts models of programs and resources to assist system designers through design-space exploration for the accelerated system.