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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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Optimizing an Algorithm for Data Mining a Design Repository to Automate Functional Modeling
The purpose of this research is to find the optimum values for threshold variables used in a data mining and prediction algorithm. We also minimize and stratify a training set to find the optimum size based on how well it represents the whole dataset. Our specific focus is automating functional models, but the method can be applied to any dataset with a similar structure. We iterate through different values for two of the threshold variables in this process and cross-validate to calculate the average accuracy and find the optimum values for each variable. We optimize the training set by reducing the size by 78% and stratifying the data, whereby we achieve an accuracy that is 96% as good as the whole training set and takes 50% less time. These optimum values can be used to better predict the functions and flows of any future product based on its constituent components, which can be used to generate a complete functional model.
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- Award ID(s):
- 1826469
- PAR ID:
- 10295088
- Date Published:
- Journal Name:
- ASME IDETC/CIE 2020
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Variability-induced accuracy degradation of RRAMbased 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 and 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.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1115/DETC2020-22346
