Stochastic Functional Verification of DNN Design through Progressive Virtual Dataset Generation

Jinhang Choi, Kevin M. Irick, Justin Hardin, Weichao Qiu, Alan Yuille, Jack Sampson, Vijaykrishnan Narayanan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Deep Neural Networks have emerged as state-of-the-art solutions for complex intelligence problems. DNNs derive their predictive power by learning from millions of training examples in either a supervised or semi-supervised fashion. As such, a critical aspect of the DNN system design procedure is the collection of large annotated training datasets that exhibit high coverage of the problem space. While data synthesis and annotation techniques have been proposed to mitigate the burden of acquiring large datasets, these methods do not quantify the usefulness of each generated dataset and its subsequent impact on training effort. In this work we establish parallels between the autonomous design of DNNs for machine vision applications and the task of functionally verifying a hardware design. Similar to automatic test vector generation, we propose a technique that progressively generates training datasets using virtual synthetic models. Furthermore, we propose an automated DNN design framework that jointly tries to stochastically maximize training coverage while minimizing the number of training and validation cycles utilizing insights from functional verification.

Original languageEnglish (US)
Title of host publication2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538648810
DOIs
StatePublished - Apr 26 2018
Event2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Florence, Italy
Duration: May 27 2018May 30 2018

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
Volume2018-May
ISSN (Print)0271-4310

Other

Other2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018
CountryItaly
CityFlorence
Period5/27/185/30/18

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Stochastic Functional Verification of DNN Design through Progressive Virtual Dataset Generation'. Together they form a unique fingerprint.

Cite this