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Found 4 papers, 0 papers with code
Fine-Grained Embedding Dimension Optimization During Training for Recommender Systems
Huge embedding tables in modern Deep Learning Recommender Models (DLRM) require prohibitively large memory during training and inference.
Planning low-carbon distributed power systems: Evaluating the role of energy storage
This paper introduces a mathematical formulation of energy storage systems into a generation capacity expansion framework to evaluate the role of energy storage in the decarbonization of distributed power systems.
DASNet: Dynamic Activation Sparsity for Neural Network Efficiency Improvement
In addition to conventional compression techniques, e. g., weight pruning and quantization, removing unimportant activations can reduce the amount of data communication and the computation cost.
HyPar: Towards Hybrid Parallelism for Deep Learning Accelerator Array
In this paper, inspired by recent work in machine learning systems, we propose a solution HyPar to determine layer-wise parallelism for deep neural network training with an array of DNN accelerators.
