Controller area networks (CANs) are widely adopted in real-time automotive control and are increasingly standard in factory automation. Considering their critical application in safety-critical systems, The error rate of the system must be accurately predicted and guaranteed. Through simulation, it is possible to obtain a low-precision overview of the system’s behavior. However, for low-probability events, the required number of samples in simulation increases rapidly, making it difficult to conduct a sufficient number of simulations in practical applications, and the statistical results may deviate from the actual outcomes. Therefore, a formal analysis is needed to evaluate the error rate of the system. This article improves the worst case probability response time analysis by using convolution-based busy window and backlog techniques under the error retransmission protocol of CANs. Empirical analysis shows that the proposed method improves upon existing methods in terms of accuracy and efficiency.

Digital images captured by unstable imaging systems often simultaneously suffer from random noise and stripe noise. Due to the complex noise distribution, denoising and destriping methods based on simple handcrafted priors may leave residual noise. Although supervised methods have achieved some progress, they rely on large-scale noisy-clean image pairs, which are challenging to obtain in practice. To address these problems, we propose a self-supervised image denoising and destriping method based on blind-spot regularization, named Self-BSR. This method transforms the overall denoising and destriping problem into a modeling task for two spatially correlated signals: image and stripe. Specifically, blind-spot regularization leverages spatial continuity learned by the improved blind-spot network to separately constrain the reconstruction of image and stripe while suppressing pixel-wise independent noise. This regularization has two advantages: first, it is adaptively formulated based on implicit network priors, without any explicit parametric modeling of image and noise; second, it enables Self-BSR to learn denoising and destriping only from noisy images. In addition, we introduce the directional feature unshuffle in Self-BSR, which extracts multi-directional information to provide discriminative features for separating image from stripe. Furthermore, the feature-resampling refinement is proposed to improve the reconstruction ability of Self-BSR by resampling pixels with high spatial correlation in the receptive field. Extensive experiments on synthetic and real-world datasets demonstrate significant advantages of the proposed method over existing methods in denoising and destriping performance. The code will be publicly available at https://github.com/Jocobqc/Self-BSR.

As the complexity of very-large-scale integration (VLSI) increases, empirically determining the design constraints necessary to achieve the optimal performance, power, and area (PPA) within the electronic design automation (EDA) workflow becomes more challenging. Design space exploration is capable of effectively and automatically identifying the design constraints required to attain the optimal PPA in VLSI designs. However, the absence of prior knowledge can lead to less efficient explorations. This paper proposes a novel fast constraint tuning framework via transfer learning and multi-objective Bayesian optimization (MOBO) to find the optimal design constraints. Firstly, we introduce transfer learning into multi-objective Bayesian optimization by Gaussian Copula and transform the PPA data into residual observations. We propose to transfer the prior information of the implemented technologies to the advanced technology to optimize the parameter design space under the advanced technology. Secondly, we propose Gaussian process regression with an auto-encoder-based deep kernel as a surrogate model in MOBO. The auto-encoder-based deep kernel can extract more input features to make the surrogate model more precise. We employ the batch uncertainty-aware search acquisition function to improve exploration efficiency. Using this surrogate model and this acquisition function in MOBO can reduce the amount that EDA tools need to run. The average EDA tools running times of the proposed model is 204, and the average ADRS is 0.0373. Compared to state-of-the-art approaches, experiments on a CPU design reveal that a higher-quality Pareto frontier can be provided with a shorter running time.

Spin locks are widely used in embedded systems to coordinate mutually exclusive accesses to shared resources from different tasks. Although the design and analysis of locking protocols have been intensively studied forsequentialreal-time tasks, there have been few works on this topic forparallelreal-time tasks. In this paper, we study the analysis of parallel real-time tasks modeled by directed acyclic graphs (DAGs) under global fixed priority scheduling using both preemptable and non-preemptable spin locks to protect accesses to shared resources in three commonly used request serving orders (unordered, FIFO-order and priority-order). In particular, we develop a general schedulability analysis framework where the blocking time caused by resource contention is formally defined, so that the blocking analysis can be performed independently and easy to combine with the traditional interference analysis techniques. Moreover, we present a unified blocking analysis technique where the blocking time is analyzed in a scalable manner based on a linear-programming (LP) approach, making our method flexible and extendable. We conduct comprehensive experiments to evaluate our method with other the-state-of-the-art approaches for scheduling real-time parallel tasks using semaphores and spin locks.

Recent breakthroughs of Neural Architecture Search (NAS) extend the field’s research scope towards a broader range of vision tasks and more diversified search spaces. While existing NAS methods mostly design architectures on a single task, algorithms that look beyond single-task search are surging to pursue a more efficient and universal solution across various tasks. Many of them leverage transfer learning and seek to preserve, reuse, and refine network design knowledge to achieve higher efficiency in future tasks. However, the enormous computational cost and experiment complexity of cross-task NAS are imposing barriers for valuable research in this direction. Existing NAS benchmarks all focus on one type of vision task, i.e., classification. In this work, we propose TransNAS-Bench-101, a benchmark dataset containing network performance across seven tasks, covering classification, regression, pixel-level prediction, and self-supervised tasks. This diversity provides opportunities to transfer NAS methods among tasks and allows for more complex transfer schemes to evolve. We explore two fundamentally different types of search space: cell-level search space and macro-level search space. With 7,352 backbones evaluated on seven tasks, 51,464 trained models with detailed training information are provided. With TransNAS-Bench-101, we hope to encourage the advent of exceptional NAS algorithms that raise cross-task search efficiency and generalizability to the next level. Our dataset and code will be available at Mindspore1and VEGA2.

Recently, some works found an interesting phenomenon that adversarially robust classifiers can generate good images comparable to generative models. We investigate this phenomenon from an energy perspective and provide a novel explanation. We reformulate adversarial example generation, adversarial training, and image generation in terms of an energy function. We find that adversarial training contributes to obtaining an energy function that is flat and has low energy around the real data, which is the key for generative capability. Based on our new understanding, we further propose a better adversarial training method, Joint Energy Adversarial Training (JEAT), which can generate high-quality images and achieve new state-of-the-art robustness under a wide range of attacks. The Inception Score of the images (CIFAR-10) generated by JEAT is 8.80, much better than original robust classifiers (7.50). In particular, we find that the robustness of JEAT is better than other hybrid models.

Parallel tasks have been paid growing attention in recent years, and the scheduling with shared resources is of significant importance to real-time systems. As an efficient mechanism to provide mutual exclusion for parallel processing, spin-locks are ubiquitous in multi-processor real-time systems. However, the spin-locks suffer the scalability problem, and the intra-task parallelism further exacerbates the analytical pessimism. To overcome such deficiencies, we propose a Hierarchical Hybrid Locking Protocol (H2LP) under federated scheduling. The proposed H2LP integrates the classical Multiprocessor Stack Resource Policy (MSRP) and uses a token mechanism to reduce global contentions. We provide a complete analysis framework supporting both heavy and light tasks under federated scheduling and develop a blocking analysis with the state-of-the-art linear optimization technique. Empirical evaluations showed that the H2LP outperformed the other state-of-the-art locking protocols in at least configurations when considering exclusive clustering. Furthermore, our partitioned approach for light tasks can substantially improve schedulability by mitigating the over-provisioning problem.

In recent years, a variety of normalization methods have been proposed to help training neural networks, such as batch normalization (BN), layer normalization (LN), weight normalization (WN), group normalization (GN), etc. However, some necessary tools to analyze all these normalization methods are lacking. In this paper, we first propose a lemma to define some necessary tools. Then, we use these tools to make a deep analysis on popular normalization methods and obtain the following conclusions: 1) Most of the normalization methods can be interpreted in a unified framework, namely normalizing pre-activations or weights onto a sphere; 2) Since most of the existing normalization methods are scaling invariant, we can conduct optimization on a sphere with scaling symmetry removed, which can help to stabilize the training of network; 3) We prove that training with these normalization methods can make the norm of weights increase, which could cause adversarial vulnerability as it amplifies the attack. Finally, a series of experiments are conducted to verify these claims.