This paper investigates the development of self-aware mechanisms for automated vehicles, introducing the notion of an automation state estimation system. This system is capable to understand its capabilities in a given context, and can leverage that knowledge to estimate the current and near-future automation performance based on internal metrics, as well as external, static (e.g. lane geometry) and dynamic environmental elements (e.g. traffic and weather information). From an application perspective, we consider automation state estimation in the scope of automation mediation, as part of a broader and holistic mediation system, with the goal to tackle challenging aspects related to transitions of control, mode confusion, and driver engagement. We used real-world data for system design, and implemented the proposed automation estimation system in a prototype vehicle. Based on 70 hours of real-world driving, we also validated the performance of the automation state estimation for automation mediation purposes.

The VEDLIoT project targets the development of energy-efficient Deep Learning for distributed AIoT applications. A holistic approach is used to optimize algorithms while also dealing with safety and security challenges. The approach is based on a modular and scalable cognitive IoT hardware platform. Using modular microserver technology enables the user to configure the hardware to satisfy a wide range of applications. VEDLIoT offers a complete design flow for Next-Generation IoT devices required for collaboratively solving complex Deep Learning applications across distributed systems. The methods are tested on various use-cases ranging from Smart Home to Automotive and Industrial IoT appliances. VEDLIoT is an H2020 EU project which started in November 2020. It is currently in an intermediate stage with the first results available.

This work analyzes the nonlinear effects in the track and hold circuit applied in high-speed ADCs or RF sampling receiver (RX) front-ends. Non-ideal effects inside the main sampling NMOS switch are studied. Parasitic varactor and sampling on-resistance modulation effects are analyzed through frequency domain Volterra series and the EKV MOS transistor model. Polynomial curve fitting is applied showing that the on-resistance modulation dominates. Finally, a novel bootstrap circuit is proposed with a fast settling time and high bootstrap voltage in a 22 nm FD-SOI CMOS technology, with its settling time analyzed using the Elmore delay model.

Abstract With ever-increasing computational power, larger computational domains are employed and thus the data output grows as well. Writing this data to disk can become a significant part of runtime if done serially. Even if the output is done in parallel, e.g., via MPI I/O, there are many user-space parameters for tuning the performance. This paper focuses on the available parameters for the Lustre file system and the Cray MPICH implementation of MPI I/O. Experiments on the Cray XC40 Hazel Hen using a Cray Sonexion 2000 Lustre file system were conducted. In the experiments, the core count, the block size and the striping configuration were varied. Based on these parameters, heuristics for striping configuration in terms of core count and block size were determined, yielding up to a 32-fold improvement in write rate compared to the default. This corresponds to 85 GB/s of the peak bandwidth of 202.5 GB/s. The heuristics are shown to be applicable to a small test program as well as a complex application.

Monitoring of High Performance Computing (HPC) platforms is critical to successful operations, can provide insights into performance-impacting conditions, and can inform methodologies for improving science throughput. However, monitoring systems are not generally considered core capabilities in system requirements specifications nor in vendor development strategies. In this paper we present work performed at a number of large-scale HPC sites towards developing monitoring capabilities that fill current gaps in ease of problem identification and root cause discovery. We also present our collective views, based on the experiences presented, on needs and requirements for enabling development by vendors or users of effective sharable end-to-end monitoring capabilities.