76 citations · 214 across the 9 of their papers we have counts for
4 papers · 1 filter
Incomplete Dot Products for Dynamic Computation Scaling in Neural Network Inference
Bradley McDanel, Surat Teerapittayanon, H. T. Kung
We propose the use of incomplete dot products (IDP) to dynamically adjust the number of input channels used in each layer of a convolutional neural network during feedforward infer…
Embedded Binarized Neural Networks
Bradley McDanel, Surat Teerapittayanon, H. T. Kung
We study embedded Binarized Neural Networks (eBNNs) with the aim of allowing current binarized neural networks (BNNs) in the literature to perform feedforward inference efficiently…
Distributed Deep Neural Networks over the Cloud, the Edge and End Devices
Surat Teerapittayanon, Bradley McDanel, H. T. Kung
We propose distributed deep neural networks (DDNNs) over distributed computing hierarchies, consisting of the cloud, the edge (fog) and end devices. While being able to accommodate…
BranchyNet: Fast Inference via Early Exiting from Deep Neural Networks
Surat Teerapittayanon, Bradley McDanel, H. T. Kung
Deep neural networks are state of the art methods for many learning tasks due to their ability to extract increasingly better features at each network layer. However, the improved…