78 citations · 160 across the 13 of their papers we have counts for
3 papers · 1 filter
Themis: A Network Bandwidth-Aware Collective Scheduling Policy for Distributed Training of DL Models
Saeed Rashidi, William Won, Sudarshan Srinivasan +2
Distributed training is a solution to reduce DNN training time by splitting the task across multiple NPUs (e.g., GPU/TPU). However, distributed training adds communication overhead…
LIBRA: Enabling Workload-aware Multi-dimensional Network Topology Optimization for Distributed Training of Large AI Models
William Won, Saeed Rashidi, Sudarshan Srinivasan +1
As model sizes in machine learning continue to scale, distributed training is necessary to accommodate model weights within each device and to reduce training time. However, this c…
Extending Sparse Tensor Accelerators to Support Multiple Compression Formats
Eric Qin, Geonhwa Jeong, William Won +7
Sparsity, which occurs in both scientific applications and Deep Learning (DL) models, has been a key target of optimization within recent ASIC accelerators due to the potential mem…