Microcontrollers running safety-critical applications with high integrity requirements must provide appropriate safety measures to manage random hardware faults. For instance, automotive safety regulations (e.g., ISO26262) impose the use of diverse redundancy for items at the highest automotive safety integrity level (ASIL), ASIL-D. In the case of computing cores, this is realized with dual core lockstep (DCLS). The advent of the RISC-VISA has made open source hardware gain popularity. However, there are few industrial open source SoCs meeting the requirements of safety-critical systems, and, to our knowledge, none of them provides lockstep cores. This paper presents the realization of a RISC-V open source lockstep core based on Gaisler's NOEL-V core for the space domain, as well as its integration in the SELENE SoC that provides a complete microcontroller synthesizable on FPGA successfully assessed against space, automotive and railway safety-critical applications in the past.

RISC-V Instruction Set Architecture (ISA) emerges as an opportunity to develop open source hardware without being subject to expensive licenses or export restrictions. A plethora of initiatives are nowadays developing systems-on-chip (SoCs) and its components based on RISC-V targeting a wide variety of markets. However, domains with safety requirements, such as avionics, space, and automotive, impose SoCs to include support to meet those requirements.This work introduces the SafeX family of components, a set of components providing SoC controllability, observability and safety measures support. These components, developed by the Barcelona Supercomputing Center with permissive open source licenses, are intended to be the basis to make SoCs meet the needs of domains with safety requirements. In particular, the SafeX components developed so far include the SafeSU (multicore statistics unit), the SafeTI (flexible and programmable traffic injector), the SafeDE and SafeSoftDR (hardware and software modules to enforce lockstep execution), and the SafeDM (module to monitor diversity across cores).

With the development of society and the rise of people's environmental awareness, air pollution is receiving increased public attention. Accurate air quality prediction can provide useful information for government decision-making and residents' activities. However, accurately predicting future air quality remains a challenging task because of the complex spatial-temporal dependencies of air quality. Previous studies failed to explicitly model these spatial-temporal dependencies. In this paper, we propose a self-adaptive spatial-temporal network (SA-STNet) to efficiently and effectively capture the spatial-temporal dependencies of air quality. In order to effectively aggregate spatial information, we employ a self-adaptive graph convolution module that can learn the latent spatial correlations of air quality automatically. In the temporal dimension, we utilise three independent components to capture the recent, daily-periodic, and weekly-periodic temporal dependencies of air quality, respectively. In addition, our model exploits rich external complementary information by means of a features extraction component. A parametric-matrix-based fusion architecture is used to combine the outputs of different components into a joint representation which is used for generating the final prediction results. Extensive experiments carried out on real-world datasets demonstrate the outstanding performance of our model compared with baselines and state-of-the-art methods.

Air pollution is a serious environmental problem that has attracted much attention. Predicting air pollutant concentration can provide useful information for urban environmental governance decision-making and residents’ daily health control. However, existing methods fail to model the temporal dependencies or have suffer from a weak ability to capture the spatial correlations of air pollutants. In this paper, we propose a general approach to predict air pollutant concentration, named DSTFN, which consists of a data completion component, a similar region selection component, and a deep spatial-temporal fusion network. The data completion component uses tensor decomposition method to complete the missing data of historical air quality. The similar region selection component uses region metadata to calculate the spatial similarity between regions. The deep spatial-temporal fusion network fuses urban heterogeneous data to capture factors affecting air quality and predict air pollutant concentration. Extensive experiments on a real-world dataset demonstrate that our model achieves the highest performance compared with state-of-the-art models for air quality prediction.