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Found 6 papers, 3 papers with code
Multi-Precision Policy Enforced Training (MuPPET) : A Precision-Switching Strategy for Quantised Fixed-Point Training of CNNs
Large-scale convolutional neural networks (CNNs) suffer from very long training times, spanning from hours to weeks, limiting the productivity and experimentation of deep learning practitioners.
Low-Cost On-device Partial Domain Adaptation (LoCO-PDA): Enabling efficient CNN retraining on edge devices
Consequently, it is likely that the observed data distribution upon deployment is a subset of the training data distribution.
perf4sight: A toolflow to model CNN training performance on Edge GPUs
The increased memory and processing capabilities of today's edge devices create opportunities for greater edge intelligence.
Caffe Barista: Brewing Caffe with FPGAs in the Training Loop
CNN training on FPGAs is a nascent field of research.
Multi-Precision Policy Enforced Training (MuPPET): A precision-switching strategy for quantised fixed-point training of CNNs
Large-scale convolutional neural networks (CNNs) suffer from very long training times, spanning from hours to weeks, limiting the productivity and experimentation of deep learning practitioners.
Now that I can see, I can improve: Enabling data-driven finetuning of CNNs on the edge
In today's world, a vast amount of data is being generated by edge devices that can be used as valuable training data to improve the performance of machine learning algorithms in terms of the achieved accuracy or to reduce the compute requirements of the model.
