Optimal DNN primitive selection with partitioned boolean quadratic programming

Published: Jan 1, 2018
Abstract
Deep Neural Networks (DNNs) require very large amounts of computation both for training and for inference when deployed in the field. Many different algorithms have been proposed to implement the most computationally expensive layers of DNNs. Further, each of these algorithms has a large number of variants, which offer different trade-offs of parallelism, data locality, memory footprint, and execution time. In addition, specific algorithms...
Paper Details
Title
Optimal DNN primitive selection with partitioned boolean quadratic programming
Published Date
Jan 1, 2018
Journal
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