An official website of the United States government
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
Efficient Low Cost Alternative Testing of Analog Crossbar Arrays for Deep Neural Networks
Analog crossbar arrays have recently attracted significant attention due to their usefulness for deep neural net (DNN) computations with ultra-low power consumption. However, recent studies have shown that DNNs implemented with such crossbar arrays suffer from as high as 30% degradation in performance due to the effects of manufacturing process variability effects resulting in degradation of their functional safety. One way to test these DNNs is to apply an exhaustive set of test images to each device to ascertain its performance. This is expensive and time-consuming. We propose an alternative test scheme in which a small subset of test images is applied to each DNN and the classification accuracy of the DNN is predicted directly from observation of the final layer outputs of the network. This saves test cost while allowing binning of DNNs for performance. Experimental results for a variety of test cases are presented and show test efficiency improvements of 3X over testing with the exhaustive test image set.
more »
« less
- Award ID(s):
- 2128419
- PAR ID:
- 10358563
- Date Published:
- Journal Name:
- International Test Conference
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
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
-
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.more » « less
-
In this work, we investigate various non-ideal effects (Stuck-At-Fault (SAF), IR-drop, thermal noise, shot noise, and random telegraph noise)of ReRAM crossbar when employing it as a dot-product engine for deep neural network (DNN) acceleration. In order to examine the impacts of those non-ideal effects, we first develop a comprehensive framework called PytorX based on main-stream DNN pytorch framework. PytorX could perform end-to-end training, mapping, and evaluation for crossbar-based neural network accelerator, considering all above discussed non-ideal effects of ReRAM crossbar together. Experiments based on PytorX show that directly mapping the trained large scale DNN into crossbar without considering these non-ideal effects could lead to a complete system malfunction (i.e., equal to random guess) when the neural network goes deeper and wider. In particular, to address SAF side effects, we propose a digital SAF error correction algorithm to compensate for crossbar output errors, which only needs one-time profiling to achieve almost no system accuracy degradation. Then, to overcome IR drop effects, we propose a Noise Injection Adaption (NIA) methodology by incorporating statistics of current shift caused by IR drop in each crossbar as stochastic noise to DNN training algorithm, which could efficiently regularize DNN model to make it intrinsically adaptive to non-ideal ReRAM crossbar. It is a one-time training method without the request of retraining for every specific crossbar. Optimizing system operating frequency could easily take care of rest non-ideal effects. Various experiments on different DNNs using image recognition application are conducted to show the efficacy of our proposed methodology.more » « less
-
The reliability of emerging neuromorphic compute fabrics is of great concern due to their widespread use in critical data-intensive applications. Ensuring such reliability is difficult due to the intensity of underlying computations (billions of parameters), errors induced by low power operation and the complex relationship between errors in computations and their effect on network performance accuracy. We study the problem of designing error-resilient neuromorphic systems where errors can stem from: (a) soft errors in computation of matrix-vector multiplications and neuron activations, (b) malicious trojan and adversarial security attacks and (c) effects of manufacturing process variations on analog crossbar arrays that can affect DNN accuracy. The core principle of error detection relies on embedded predictive neuron checks using invariants derived from the statistics of nominal neuron activation patterns of hidden layers of a neural network. Algorithmic encodings of hidden neuron function are also used to derive invariants for checking. A key contribution is designing checks that are robust to the inherent nonlinearity of neuron computations with minimal impact on error detection coverage. Once errors are detected, they are corrected using probabilistic methods due to the difficulties involved in exact error diagnosis in such complex systems. The technique is scalable across soft errors as well as a range of security attacks. The effects of manufacturing process variations are handled through the use of compact tests from which DNN performance can be assessed using learning techniques. Experimental results on a variety of neuromorphic test systems: DNNs, spiking networks and hyperdimensional computing are presented.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
