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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
Funky: Cloud-Native FPGA Virtualization and Orchestration
Atsushi Koshiba, Charalampos Mainas, Pramod Bhatotia · 2025-10-17 · via cs.DC updates on arXiv.org

The adoption of FPGAs in cloud-native environments is facing impediments due to FPGA limitations and CPU-oriented design of orchestrators, as they lack virtualization, isolation, and preemption support for FPGAs. Consequently, cloud providers offer no orchestration services for FPGAs, leading to low scalability, flexibility, and resiliency. This paper presents Funky, a full-stack FPGA-aware orchestration engine for cloud-native applications. Funky offers primary orchestration services for FPGA workloads to achieve high performance, utilization, scalability, and fault tolerance, accomplished by three contributions: (1) FPGA virtualization for lightweight sandboxes, (2) FPGA state management enabling task preemption and checkpointing, and (3) FPGA-aware orchestration components following the industry-standard CRI/OCI specifications. We implement and evaluate Funky using four x86 servers with Alveo U50 FPGA cards. Our evaluation highlights that Funky allows us to port 23 OpenCL applications from the Xilinx Vitis and Rosetta benchmark suites by modifying 3.4% of the source code while keeping the OCI image sizes 28.7 times smaller than AMD's FPGA-accessible Docker containers. In addition, Funky incurs only 7.4% performance overheads compared to native execution, while providing virtualization support with strong hypervisor-enforced isolation and cloud-native orchestration for a set of distributed FPGAs. Lastly, we evaluate Funky's orchestration services in a large-scale cluster using Google production traces, showing its scalability, fault tolerance, and scheduling efficiency.