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Title: EGIS: Entropy Guided Image Synthesis for Dataset-Agnostic Testing of RRAM-Based DNNs
While resistive random access memory (RRAM) based deep neural networks (DNN) are important for low-power inference in IoT and edge applications, they are vulnerable to the effects of manufacturing process variations that degrade their performance (classification accuracy). However, to test the same post-manufacture, the (image) dataset used to train the associated machine learning applications may not be available to the RRAM crossbar manufacturer for privacy reasons. As such, the performance of DNNs needs to be assessed with carefully crafted dataset-agnostic synthetic test images that expose anomalies in the crossbar manufacturing process to the maximum extent possible. In this work, we propose a dataset-agnostic post-manufacture testing framework for RRAM-based DNNs using Entropy Guided Image Synthesis (EGIS). We first create a synthetic image dataset such that the DNN outputs corresponding to the synthetic images minimize an entropy-based loss metric. Next, a small subset (consisting of 10-20 images) of the synthetic image dataset, called the compact image dataset, is created to expedite testing. The response of the device under test (DUT) to the compact image dataset is passed to a machine learning based outlier detector for pass/fail labeling of the DUT. It is seen that the test accuracy using such synthetic test images is very close to that of contemporary test methods.  more » « less
Award ID(s):
2414361
PAR ID:
10586788
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
IEEE
Date Published:
Edition / Version:
1
Volume:
1
Issue:
1
Page Range / eLocation ID:
1-6
Subject(s) / Keyword(s):
DNN Testing, RRAM, Test images
Format(s):
Medium: X Size: 1 Other: 1
Size(s):
1
Location:
Lyon, France
Sponsoring Org:
National Science Foundation
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