Scaling AI with Dynamic Inference Paths in Neural Networks Introduction IBM Research, with the help of the University of Texas Austin and the University of Maryland, has tried to expedite the performance of neural networks by creating a technology, called BlockDrop. Behind the design of this technology lies the objective and promise of speeding up convolutional neural network operations without any loss of fidelity, which can offer a great savings of cost to the ML […]
