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HyperParallel-MoE: Multi-Core Interleaved Scheduling for Fast MoE Training on Ascend NPUs
arXiv:2605.23764v2 Announce Type: replace Abstract: Modern Mixture-of-Experts (MoE) models increasingly rely on large-scale AI accelerator clusters for efficient training. Ascend NPUs expose heterogeneous on-chip compute resources, including matrix-oriented AIC units and vector-oriented AIV units with explicit cross-queue synchronization support. However, existing training frameworks largely execute MoE operators in a serialized kernel-by-kernel manner, leaving substantial heterogeneous...
Artificial Intelligence Consortium minutes – February 2026
The Artificial Intelligence Consortium (AIC) aims to provide a platform for public-private engagement to further dialogue on the capabilities, development, deployment, use, and potential risks of artificial intelligence (AI) in UK financial services.
AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments
arXiv:2606.01161v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have achieved remarkable success in various applications. Sampling-based GNN training, which conducts mini-batch training on sampled subgraphs, has become a promising solution for large-scale graphs. Given the resource-intensive nature of sampling-based GNN training, Neural Processing Units (NPUs), such as the Ascend AI processor, offer a promising alternative due to their high throughput and energy efficiency,...
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Bayesian estimation of spectral parameters of the 6.7-GHz methanol maser G339.884-1.259 from GRAO observations
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