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End-to-end performance estimation and measurement of deep neural network(DNN) systems become more important with increasing complexity of DNN systemsconsisting of hardware and software components. The methodology proposed inthis paper aims at a reduced turn-around time for evaluating different designchoices of hardware and software components of DNN systems. This reduction isachieved by moving the performance estimation from the implementation phase tothe concept phase by employing virtual hardware models instead of gatheringmeasurement results from physical prototypes. Deep learning compilers introducehardware-specific transformations and are, therefore, considered a part of thedesign flow of virtual system models to extract end-to-end performanceestimations. To validate the run-time accuracy of the proposed methodology, asystem processing the DilatedVGG DNN is realized both as virtual system modeland as hardware implementation. The results show that up to 92 % accuracy canbe reached in predicting the processing time of the DNN inference.

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