Joint Program and Layout Transformations to enable Convolutional Operators on Specialized Hardware based on Constraint Programming

no code implementations • 10 Apr 2021 • Dennis Rieber, Axel Acosta, Holger Fröning

First solutions to this problem have been proposed, such as TVM, UNIT or ISAMIR, which work on a loop-level representation of operators and specify data layout and possible program transformations before the embedding into the operator is performed.

An End-to-End HW/SW Co-Design Methodology to Design Efficient Deep Neural Network Systems using Virtual Models

no code implementations • 25 Oct 2019 • Michael J. Klaiber, Sebastian Vogel, Axel Acosta, Robert Korn, Leonardo Ecco, Kristine Back, Andre Guntoro, Ingo Feldner

End-to-end performance estimation and measurement of deep neural network (DNN) systems become more important with increasing complexity of DNN systems consisting of hardware and software components.