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Variability-induced accuracy degradation of RRAM based DNNs is of great concern due to their significant potential for use in future energy-efficient machine learning architectures. To address this, we propose a two-step process. First, an enhanced testing procedure is used to predict DNN accuracy from a set of compact test stimuli (images). This test response (signature) is simply the concatenated vectors of output neurons of intermediate final DNN layers over the compact test images applied. DNNs with a predicted accuracy below a threshold are then tuned based on this signature vector. Using a clustering based approach, the signature is mapped to the optimal tuning parameter values of the DNN (determined using off-line training of the DNN via backpropagation) in a single step, eliminating any post-manufacture training of the DNN weights (expensive). The tuning parameters themselves consist of the gains and offsets of the ReLU activation of neurons of the DNN on a per-layer basis and can be tuned digitally. Tuning is achieved in less than a second of tuning time, with yield improvements of over 45% with a modest accuracy reduction of 4% compared to digital DNNs.
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This content will become publicly available on March 31, 2026
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.
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- Award ID(s):
- 2414361
- PAR ID:
- 10586788
- 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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