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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
Single Event Effects Assessment of UltraScale+ MPSoC Syst...
Dimitris Agiakatsikas, Nikos Foutris, Aitzan Sari, Vasileios Vla · 2023-02-21 · via cs.DC updates on arXiv.org

The AMD UltraScale+ XCZU9EG device is a Multi-Processor System-on-Chip (MPSoC) with embedded Programmable Logic (PL) that excels in many Edge (e.g., automotive or avionics) and Cloud (e.g., data centres) terrestrial applications. However, it incorporates a large amount of SRAM cells, making the device vulnerable to Neutron-induced Single Event Upsets (NSEUs) or otherwise soft errors. Semiconductor vendors incorporate soft error mitigation mechanisms to recover memory upsets (i.e., faults) before they propagate to the application output and become an error. But how effective are the MPSoC's mitigation schemes? Can they effectively recover upsets in high altitude or large scale applications under different workloads? This article answers the above research questions through a solid study that entails accelerated neutron radiation testing and dependability analysis. We test the device on a broad range of workloads, like multi-threaded software used for pose estimation and weather prediction or a software/hardware (SW/HW) co-design image classification application running on the AMD Deep Learning Processing Unit (DPU). Assuming a one-node MPSoC system in New York City (NYC) at 40k feet, all tested software applications achieve a Mean Time To Failure (MTTF) greater than 148 months, which shows that upsets are effectively recovered in the processing system of the MPSoC. However, the SW/HW co-design (i.e., DPU) in the same one-node system at 40k feet has an MTTF = 4 months due to the high failure rate of its PL accelerator, which emphasises that some MPSoC workloads may require additional NSEU mitigation schemes. Nevertheless, we show that the MTTF of the DPU can increase to 87 months without any overhead if one disregards the failure rate of tolerable errors since they do not affect the correctness of the classification output.