Reduce Memory by 75% Using D‑CHAG’s Cross‑Channel Hierarchical Aggregation

Researchers at Oak Ridge National Laboratory introduced D‑CHAG, a distributed cross‑channel hierarchical aggregation method that cuts memory consumption by up to 75% and more than doubles throughput when training massive multi‑channel foundation models on up to 1,024 AMD GPUs, as demonstrated on hyperspectral imaging and weather‑forecasting workloads.

D-CHAGDistributed TrainingMemory Optimization

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Reduce Memory by 75% Using D‑CHAG’s Cross‑Channel Hierarchical Aggregation

Alimama Tech

Nov 10, 2021 · Artificial Intelligence

AutoHERI: Hierarchical Representation Automatic Aggregation for CVR Estimation in Advertising

AutoHERI, a hierarchical representation automatic aggregation model discovered via one‑shot neural architecture search, jointly learns CTR and CVR (and other downstream tasks) to capture cascade relationships, achieving superior AUC and conversion‑rate lifts in large‑scale Alibaba advertising datasets and prompting full production deployment.

AutoMLCVR estimationNeural Architecture Search

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AutoHERI: Hierarchical Representation Automatic Aggregation for CVR Estimation in Advertising