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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
Achieving both High Energy Efficiency and High Performanc...
Rachata Ausavarungnirun, Chris Fallin, Xiangyao Yu, Kevin Kai-We · 2016-02-19 · via cs.DC updates on arXiv.org

Hierarchical ring networks, which hierarchically connect multiple levels of rings, have been proposed in the past to improve the scalability of ring interconnects, but past hierarchical ring designs sacrifice some of the key benefits of rings by introducing more complex in-ring buffering and buffered flow control. Our goal in this paper is to design a new hierarchical ring interconnect that can maintain most of the simplicity of traditional ring designs (no in-ring buffering or buffered flow control) while achieving high scalability as more complex buffered hierarchical ring designs. Our design, called HiRD (Hierarchical Rings with Deflection), includes features that allow us to mostly maintain the simplicity of traditional simple ring topologies while providing higher energy efficiency and scalability. First, HiRD does not have any buffering or buffered flow control within individual rings, and requires only a small amount of buffering between the ring hierarchy levels. When inter-ring buffers are full, our design simply deflects flits so that they circle the ring and try again, which eliminates the need for in-ring buffering. Second, we introduce two simple mechanisms that provides an end-to-end delivery guarantee within the entire network without impacting the critical path or latency of the vast majority of network traffic. HiRD attains equal or better performance at better energy efficiency than multiple versions of both a previous hierarchical ring design and a traditional single ring design. We also analyze our design's characteristics and injection and delivery guarantees. We conclude that HiRD can be a compelling design point that allows higher energy efficiency and scalability while retaining the simplicity and appeal of conventional ring-based designs.