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

TL;DR

This paper presents a methodology for rapid performance estimation of DNN systems using virtual hardware models, enabling early design decisions with high accuracy, thus reducing development time.

Contribution

It introduces an end-to-end co-design approach employing virtual models for early performance estimation of DNN systems, integrating deep learning compilers into the virtual design flow.

Findings

Achieves up to 92% accuracy in predicting DNN inference processing time.

Reduces design iteration time by moving performance estimation to the concept phase.

Validates methodology with a DilatedVGG DNN system.

Abstract

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. The methodology proposed in this paper aims at a reduced turn-around time for evaluating different design choices of hardware and software components of DNN systems. This reduction is achieved by moving the performance estimation from the implementation phase to the concept phase by employing virtual hardware models instead of gathering measurement results from physical prototypes. Deep learning compilers introduce hardware-specific transformations and are, therefore, considered a part of the design flow of virtual system models to extract end-to-end performance estimations. To validate the run-time accuracy of the proposed methodology, a system processing the DilatedVGG DNN is realized both as virtual system model and as hardware implementation. The results show that up to 92 % accuracy can be reached in predicting the processing time of the DNN inference.