Query: CNN microprocessor critical-safety

title: Towards a Safety Case for Hardware Fault Tolerance in Convolutional Neural Networks Using Activation Range Supervision
abstract: Convolutional neural networks (CNNs) have become an established part of numerous safetycritical computer vision applications, including human robot interactions and automated driving. Real world implementations will need to guarantee their robustness against hardware soft errors corrupting the underlying platform memory. Based on the previously observed efficacy of activation clipping techniques, we build a prototypical safety case for classifier CNNs by demonstrating that range supervision represents a highly reliable fault detector and mitigator with respect to relevant bit flips, adopting an eight exponent floating point data representation. We further explore novel, non uniform range restriction methods that effectively suppress the probability of silent data corruptions and uncorrectable errors. As a safety relevant end to end use case, we showcase the benefit of our approach in a vehicle classification scenario, using ResNet50 and the traffic camera data set MIOVision. The quantitative evidence provided in this work can be leveraged to inspire further and possibly more complex CNN safety arguments. 1 Motivation With the widespread use of convolutional neural networks (CNN) across many safety critical domains such as automated robots and cars, one of the most prevailing challenges is the establishment of a safety certification for such artificial intelligence (AI) components, e.g. with respect to the ISO 26262  or ISO/PAS 21448 (SOTIF)  This certification requires not only a high fault tolerance of the trained network against unknown or adversarial input, but also efficient protection against hardware faults of the underlying platform [3, 4] Importantly this includes transient soft errors, meaning disturbances originating from events such as cosmic neutron radiation, isotopes emitting alpha particles, or electromagnetic leakage on the computer circuitry itself. *corresponding author,   Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0) G1: System is sufficiently safe in

title: Evaluating the Robustness of Bayesian Neural Networks Against Different Types of Attacks
abstract: To evaluate the robustness gain of Bayesian neural networks on image classification tasks, we perform input perturbations, and adversarial attacks to the state of the art Bayesian neural networks, with a benchmark CNN model as reference. The attacks are selected to simulate signal interference and cyberattacks towards CNN based machine learning systems. The result shows that a Bayesian neural network achieves significantly higher robustness against adversarial attacks generated against a deterministic neural network model, without adversarial training. The Bayesian posterior can act as the safety precursor of ongoing malicious activities. Furthermore, we show that the stochastic classifier after the deterministic CNN extractor has sufficient robustness enhancement rather than a stochastic feature extractor before the stochastic classifier. This advises on utilizing stochastic layers in building decision making pipelines within a safety critical domain.

title: Ensemble of Pruned Networks for Reliable Classifiers
abstract: Convolutional Neural Networks (CNNs) have been widely used for image recognition or natural language processing. When CNNs are used in safety critical applications, their reliability becomes a priority and in particular their tolerance to soft errors. Unfortunately, traditional fault tolerant techniques based on modular redundancy introduce a large overhead, which is not acceptable in many resource limited embedded systems given the complexity of CNNs. To reduce the cost of protecting CNNs, the use of an ensemble of smaller CNNs has been recently proposed. The idea is that the ensemble cannot only improve the classification accuracy but also protect against errors. To reduce the cost, pruning the CNNs is widely used. In this paper, the combination of both ideas is explored, namely ensembles of CNNs that are built using different pruned versions of the same CNN are analyzed. To evaluate the effectiveness of the idea, VGG16 is used as a case study. Small networks with different sizes are generated by pruning the original VGG16, and a combiner was designed with the ability to detect a faulty base learner. The reliability of different ensembles of the pruned networks is evaluated in PyTorch on Cifar 10. The reliability evaluation results reveal that the ensemble of pruned networks could achieve higher reliability than the Triple Modular Redundancy (TMR) protected VGG16 networks with an overhead lower than double modular redundancy. Complementarity analysis between different pruned VGG nets has been performed to explain the reliability improvement.

title: Advancements in Microprocessor Architecture for Ubiquitous AI An Overview on History, Evolution, and Upcoming Challenges in AI Implementation
abstract: Artificial intelligence (AI) has successfully made its way into contemporary industrial sectors such as automobiles, defense, industrial automation 4.0, healthcare technologies, agriculture, and many other domains because of its ability to act autonomously without continuous human interventions. However, this capability requires processing huge amounts of learning data to extract useful information in real time. The buzz around AI is not new, as this term has been widely known for the past half century. In the 1960s, scientists began to think about machines acting more like humans, which resulted in the development of the first natural language processing computers. It laid the foundation of AI, but there were only a handful of applications until the 1990s due to limitations in processing speed, memory, and computational power available. Since the 1990s, advancements in computer architecture and memory organization have enabled microprocessors to deliver much higher performance. Simultaneously, improvements in the understanding and mathematical representation of AI gave birth to its subset, referred to as machine learning (ML) ML includes different algorithms for independent learning, and the most promising ones are based on brain inspired techniques classified as artificial neural networks (ANNs) ANNs have subsequently evolved to have deeper and larger structures and are often characterized as deep neural networks (DNN) and convolution neural networks (CNN) In tandem with the emergence of multicore processors, ML techniques started to be embedded in a range of scenarios and applications. Recently, application specific instruction set architecture for AI applications has also been supported in different microprocessors. Thus, continuous improvement in microprocessor capabilities has reached a stage where it is now possible to implement complex real time intelligent applications like computer vision, object identification, speech recognition, data security, spectrum sensing, etc. This paper presents an overview on the evolution of AI and how the increasing capabilities of microprocessors have fueled the adoption of AI in a plethora of application domains. The paper also discusses the upcoming trends in microprocessor architectures and how they will further propel the assimilation of AI in our daily lives.

title: Online Testing of a Row Stationary Convolution Accelerator
abstract: Convolution accelerators used in safety critical systems require robustness and resilience to runtime faults. In this paper, we design a processing element for the convolution accelerators that support a row stationary dataflow. The proposed processing element is equipped with an embedded built in mechanism for online testing of its computational parts without degrading performance by taking advantage of CNN data sparsity. Furthermore, the proposed architecture uses a low cost error detecting code to verify PE interconnections and local memory. The result obtained using post synthesis simulations reveal, on average, 34% and 30% overheads on the power and area, respectively. This is in exchange for equipping the architecture with components that detect and locate faulty processing elements at runtime.

title: Verification of Size Invariance in DNN Activations using Concept Embeddings
abstract: The benefits of deep neural networks (DNNs) have become of interest for safety critical applications like medical ones or automated driving. Here, however, quantitative insights into the DNN inner representations are mandatory [10] One approach to this is concept analysis, which aims to establish a mapping between the internal representation of a DNN and intuitive semantic concepts. Such can be sub objects like human body parts that are valuable for validation of pedestrian detection. To our knowledge, concept analysis has not yet been applied to large object detectors, specifically not for sub parts. Therefore, this work first suggests a substantially improved version of the Net2Vec approach  for post hoc segmentation of sub objects. Its practical applicability is then demonstrated on a new concept dataset by two exemplary assessments of three standard networks, including the larger Mask R CNN model [9] (1) the consistency of body part similarity, and (2) the invariance of internal representations of body parts with respect to the size in pixels of the depicted person. The findings show that the representation of body parts is mostly size invariant, which may suggest an early intelligent fusion of information in different size categories.

title: One Size Doesn't Fit All: Adaptive Label Smoothing
abstract: This paper concerns the use of objectness measures to improve the calibration performance of Convolutional Neural Networks (CNNs) CNNs have proven to be very good classifiers and generally localize objects well; however, the loss functions typically used to train classification CNNs do not penalize inability to localize an object, nor do they take into account an object's relative size in the given image. During training on ImageNet 1K almost all approaches use random crops on the images and this transformation sometimes provides the CNN with background only samples. This causes the classifiers to depend on context. Context dependence is harmful for safety critical applications. We present a novel approach to classification that combines the ideas of objectness and label smoothing during training. Unlike previous methods, we compute a smoothing factor that is \emph{adaptive} based on relative object size within an image. This causes our approach to produce confidences that are grounded in the size of the object being classified instead of relying on context to make the correct predictions. We present extensive results using ImageNet to demonstrate that CNNs trained using adaptive label smoothing are much less likely to be overconfident in their predictions. We show qualitative results using class activation maps and quantitative results using classification and transfer learning tasks. Our approach is able to produce an order of magnitude reduction in confidence when predicting on context only images when compared to baselines. Using transfer learning, we gain 0.021AP on MS COCO compared to the hard label approach.

title: Predicting catastrophic temperature changes based on past events via a CNN LSTM regression mechanism
abstract: The modelling and prediction of extreme temperature changes in enclosed compartments is a domain with applications ranging from residential fire alarms, industrial temperature sensors to search and rescue personnel safety systems. The spread of fire in enclosed compartments is a highly uncertain and nonlinear process. Hence, in safety critical cases, any false negatives pose a serious threat to the safety of individuals such as firefighters that are engaged in rescue activities. This work aims to model the nonlinear fire spread behaviour as a temporal, deep learning based regressive methodology. The objective is to efficiently identify abrupt and extreme temperature changes that often result in increases of 300+ degC. A major challenge in such time series models is that of learning from historic time series samples which are known to suffer from high noise levels, outliers and data imbalance. This work contributes on the development of a convolutional neural network (CNN) long short term memory (LSTM) methodology to handle temperature data originating from body mounted and fixed sensors and develop a temperature increase warning mechanism. The main contribution exploits the contextualisation ability of CNN LSTM to predict temperature changes in windows of 5 120 s. The model identifies the spatial temperature change patterns via a CNN encoder, which are then fed into an LSTM network. This regression mechanism is trained and validated against a set of unique fire spread conditions and involved live tests ranging from containers to residential and industrial units. The model's performance was evaluated with MAPE sensitivity analysis against data originating from body mounted sensors and third party NIST datasets. The outcome showed an error ranging from 0.89 to 2.05% to 5.46% and 6.23% respectively. The model efficacy was also evaluated against a range of input output temperature ranges from 5, 30 to 120 s windows and showed a FN rate of 2.15% for pre alarm to normal and 3.11% for alarm to pre alarm cases in body mounted sensors and a higher FN rate of 5.14% reported for pre alarm to normal case for the raised platfor sensor tests.

title: Combining Architectural Simulation and Software Fault Injection for a Fast and Accurate CNNs Reliability Evaluation on GPUs
abstract: Graphic Processing Units (GPUs) are commonly used to accelerate Convolutional Neural Networks (CNNs) for object detection and classification. As CNNs are employed in safety critical applications, such as autonomous vehicles, their reliability must be carefully evaluated. In this work, we combine the accuracy of microarchitectural simulation with the speed of software fault injection to investigate the reliability of CNNs executed in GPUs. First, with a detailed microarchitectural fault injection on a GPU model (FlexGripPlus) we characterize the effects of faults in critical and user hidden modules (such as the Warp Scheduler and the Pipeline Registers) in the computation of convolution over a suitably selected subset of tiles. Then, with software fault injection, we propagate the fault effects in the CNN. Thanks to our approach we are able, for the first time, to analyze the impact of faults affecting GPUs' hidden modules on a whole CNN execution (LeNET) without undermining the reliability evaluation correctness.

title: Dual Head Adversarial Training
abstract: Deep neural networks (DNNs) are known to be vulnerable to adversarial examples/attacks, raising concerns about their reliability in safety critical applications. A number of defense methods have been proposed to train robust DNNs resistant to adversarial attacks, among which adversarial training has so far demonstrated the most promising results. However, recent studies have shown that there exists an inherent tradeoff between accuracy and robustness in adversarially trained DNNs. In this paper, we propose a novel technique Dual Head Adversarial Training (DH AT) to further improve the robustness of existing adversarial training methods. Different from existing improved variants of adversarial training, DH AT modifies both the architecture of the network and the training strategy to seek more robustness. Specifically, DH AT first attaches a second network head (or branch) to one intermediate layer of the network, then uses a lightweight convolutional neural network (CNN) to aggregate the outputs of the two heads. The training strategy is also adapted to reflect the relative importance of the two heads. We empirically show, on multiple benchmark datasets, that DH AT can bring notable robustness improvements to existing adversarial training methods. Compared with TRADES, one state of the art adversarial training method, our DH AT can improve the robustness by 3.4% against PGD40 and 2.3% against AutoAttack, and also improve the clean accuracy by 1.8%