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SmartSAGE: Training Large-scale Graph Neural Networks using In-Storage Processing Architectures
Our work demonstrates that an ISP based large-scale GNN training system can achieve both high capacity storage and high performance, opening up opportunities for ML practitioners to train large GNN datasets without being hampered by the physical limitations of main memory size.
Tensor Casting: Co-Designing Algorithm-Architecture for Personalized Recommendation Training
Personalized recommendations are one of the most widely deployed machine learning (ML) workload serviced from cloud datacenters.
TensorDIMM: A Practical Near-Memory Processing Architecture for Embeddings and Tensor Operations in Deep Learning
Recent studies from several hyperscalars pinpoint to embedding layers as the most memory-intensive deep learning (DL) algorithm being deployed in today's datacenters.
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