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Hermes: Accelerating Long-Latency Load Requests via Perceptron-Based Off-Chip Load Prediction
Announce Type: replace Abstract: Long-latency load requests continue to limit the performance of high-performance processors. To increase the latency tolerance of a processor, architects have primarily relied on two key techniques: sophisticated data prefetchers and large on-chip caches. In this work, we show that: 1) even a sophisticated state-of-the-art prefetcher can only predict half of the off-chip load requests on average across a wide range of workloads, and 2) due to the increasing...
UltraEP: Unleash MoE Training and Inference on Rack-Scale Nodes with Near-Optimal Load Balancing
arXiv:2606.04101v1 Announce Type: new Abstract: Large-scale expert parallelism (EP) is becoming pivotal for training and serving frontier MoE models, but it also amplifies device-level expert load imbalance into compute stragglers, token all-to-all bottlenecks, and activation-memory spikes. Existing balancers redistribute experts periodically based on historical load, which becomes unreliable for production deployments with non-stationary load patterns. We present UltraEP, the first...
UltraEP: Unleash MoE Training and Inference on Rack-Scale Nodes with Near-Optimal Load Balancing
arXiv:2606.04101v2 Announce Type: replace Abstract: Large-scale expert parallelism (EP) is becoming pivotal for training and serving frontier MoE models, but it also amplifies device-level expert load imbalance into compute stragglers, token all-to-all bottlenecks, and activation-memory spikes. Existing balancers redistribute experts periodically based on historical load, which becomes unreliable for production deployments with non-stationary load patterns. We present UltraEP, the first...
Evaluating the Performance of Deep Learning Models in Whole-body Dynamic 3D Posture Prediction During Load-reaching Activities
arXiv:2511.20615v2 Announce Type: replace Abstract: This study aimed to explore the application of deep neural networks for whole-body human posture prediction during dynamic load-reaching activities. Two time-series models were trained using bidirectional long short-term memory (BLSTM) and transformer architectures. The dataset consisted of 3D full-body plug-in gait dynamic coordinates from 20 normal-weight healthy male individuals each performing 204 load-reaching tasks from different load...
Zero and Few Shot Load Forecasting with Large Language Models
arXiv:2411.11350v2 Announce Type: replace Abstract: Deep learning models have shown strong performance in load forecasting, but they generally require large amounts of data for model training before being applied to new scenarios, which limits their effectiveness in data-scarce scenarios. Inspired by the great success of pre-trained language models (LLMs) in natural language processing, this paper proposes a zero and few shot load forecasting approach using an advanced LLM framework denoted...
ICE guard lost a loaded gun inside country’s biggest detention center, government investigators say
ICE guard lost a loaded gun inside country’s biggest detention center, government investigators say Watchdog alleges ‘serious’ oversight issues at Camp East Montana after reports of homicide, tuberculosis and medical neglect - Bookmark - CommentsGo to comments A security guard inside the nation’s largest immigration detention center lost a loaded firearm that wasn’t found after two months of searches, according to a damning new report from a federal government watchdog. In January, a...
Geometric Time-Domain Identification of Three-Phase Load Equivalents from Terminal Measurements
arXiv:2606.07048v1 Announce Type: cross Abstract: This paper presents a geometric time-domain method for identifying three-phase load equivalents from instantaneous voltage and current measurements at the point of common coupling. Measured waveforms are interpreted as trajectories in Euclidean signal spaces, and load-equivalent parameters are recovered from the geometry of those trajectories. The method extends a previously published single-phase geometric identification formulation to...
Geometric Time-Domain Identification of Three-Phase Load Equivalents from Terminal Measurements
arXiv:2606.07048v2 Announce Type: replace-cross Abstract: This paper presents a geometric time-domain method for identifying three-phase load equivalents from instantaneous voltage and current measurements at the point of common coupling. Measured waveforms are interpreted as trajectories in Euclidean signal spaces, and load-equivalent parameters are recovered from the geometry of those trajectories. The method extends a previously published single-phase geometric identification formulation...
Accelerating Divisible Load Processing Through Machine Learning: A Practical Framework for Large-Scale Workloads
Announce Type: replace Abstract: In this paper, we introduce the first machine learning framework for predicting optimal processing times in Single-Level Tree Network (SLTN) architectures for the Divisible Load Theory (DLT) paradigm. Using a feedforward neural network(FNN) with 16 engineered features, we train a model on 100,000 synthetically generated configurations to predict optimal processing times without explicit formulation of DLT equations. The model achieves 97-99% accuracy...
Branch-Level Energy Localization in Three-Phase Loads: Resolving Indeterminacy in Time-Domain
arXiv:2606.07076v1 Announce Type: cross Abstract: This paper develops a branch-level energy-localization framework for three-phase loads. The instantaneous terminal power of an admissible lumped equivalent is decomposed uniquely as Joule dissipation plus magnetic and electric stored-energy rates, branch by branch. Three formal results are established: a Branch-Level Localization Theorem (uniqueness given an admissible topology); a Topology-Indeterminacy Theorem (multiple admissible...