10 citations · 39 across the 11 of their papers we have counts for
3 papers · 1 filter
Survey on Large Scale Neural Network Training
Julia Gusak, Daria Cherniuk, Alena Shilova +8
Modern Deep Neural Networks (DNNs) require significant memory to store weight, activations, and other intermediate tensors during training. Hence, many models do not fit one GPU de…
Few-Bit Backward: Quantized Gradients of Activation Functions for Memory Footprint Reduction
Georgii Novikov, Daniel Bershatsky, Julia Gusak +3
Memory footprint is one of the main limiting factors for large neural network training. In backpropagation, one needs to store the input to each operation in the computational grap…
Memory-Efficient Backpropagation through Large Linear Layers
Daniel Bershatsky, Aleksandr Mikhalev, Alexandr Katrutsa +3
In modern neural networks like Transformers, linear layers require significant memory to store activations during backward pass. This study proposes a memory reduction approach to…