1 citations · 2 across the 2 of their papers we have counts for
2 papers
cs.LG2023★ 1 cited
TinyProp -- Adaptive Sparse Backpropagation for Efficient TinyML On-device Learning
Marcus Rüb, Daniel Maier, Daniel Mueller-Gritschneder +1
Training deep neural networks using backpropagation is very memory and computationally intensive. This makes it difficult to run on-device learning or fine-tune neural networks on…
cs.LG2023★ 1 cited
Fused Depthwise Tiling for Memory Optimization in TinyML Deep Neural Network Inference
Rafael Stahl, Daniel Mueller-Gritschneder, Ulf Schlichtmann
Memory optimization for deep neural network (DNN) inference gains high relevance with the emergence of TinyML, which refers to the deployment of DNN inference tasks on tiny, low-po…