159,354 research outputs found
Validating Predictions of Unobserved Quantities
The ultimate purpose of most computational models is to make predictions,
commonly in support of some decision-making process (e.g., for design or
operation of some system). The quantities that need to be predicted (the
quantities of interest or QoIs) are generally not experimentally observable
before the prediction, since otherwise no prediction would be needed. Assessing
the validity of such extrapolative predictions, which is critical to informed
decision-making, is challenging. In classical approaches to validation, model
outputs for observed quantities are compared to observations to determine if
they are consistent. By itself, this consistency only ensures that the model
can predict the observed quantities under the conditions of the observations.
This limitation dramatically reduces the utility of the validation effort for
decision making because it implies nothing about predictions of unobserved QoIs
or for scenarios outside of the range of observations. However, there is no
agreement in the scientific community today regarding best practices for
validation of extrapolative predictions made using computational models. The
purpose of this paper is to propose and explore a validation and predictive
assessment process that supports extrapolative predictions for models with
known sources of error. The process includes stochastic modeling, calibration,
validation, and predictive assessment phases where representations of known
sources of uncertainty and error are built, informed, and tested. The proposed
methodology is applied to an illustrative extrapolation problem involving a
misspecified nonlinear oscillator
Real-Time Application Processing for FPGA-Based Resilient Embedded Systems in Harsh Environments
Real-time embedded systems nowadays get employed in harsh environments such as space, nuclear sites to carry out critical operations. Along with the traditional software based (CPU) execution, FPGAs are now also emerging as a bright prospect to accomplish such routines. However, these platforms are often get plagued by faults generated due to the high radiations in such environments. As a result, the real-time applications running on the platform could also get jeopardized. Thus, efficient execution of a set of hard real-time applications on reconfigurable systems with anomaly detection and recovery mechanism is inevitable. This work aims at tackling such problem with a âhealingâ approach for extreme environments. Initially, the applications are intelligently partitioned for hardware and software execution, then attempts have been made to schedule hardware applications with intermittent preemption point. Upon detecting any abnormality on such distinct points, our approach orchestrates a healing mechanism to remediate the scenario without hampering the pre-determined schedule. Experimental validation of our proposed method reveals its effectiveness
Efficient Simulation of Structural Faults for the Reliability Evaluation at System-Level
In recent technology nodes, reliability is considered a part of the standard design Âżow at all levels of embedded system design. While techniques that use only low-level models at gate- and register transfer-level offer high accuracy, they are too inefficient to consider the overall application of the embedded system. Multi-level models with high abstraction are essential to efficiently evaluate the impact of physical defects on the system. This paper provides a methodology that leverages state-of-the-art techniques for efficient fault simulation of structural faults together with transaction-level modeling. This way it is possible to accurately evaluate the impact of the faults on the entire hardware/software system. A case study of a system consisting of hardware and software for image compression and data encryption is presented and the method is compared to a standard gate/RT mixed-level approac
Parallel Multi-Hypothesis Algorithm for Criticality Estimation in Traffic and Collision Avoidance
Due to the current developments towards autonomous driving and vehicle active
safety, there is an increasing necessity for algorithms that are able to
perform complex criticality predictions in real-time. Being able to process
multi-object traffic scenarios aids the implementation of a variety of
automotive applications such as driver assistance systems for collision
prevention and mitigation as well as fall-back systems for autonomous vehicles.
We present a fully model-based algorithm with a parallelizable architecture.
The proposed algorithm can evaluate the criticality of complex, multi-modal
(vehicles and pedestrians) traffic scenarios by simulating millions of
trajectory combinations and detecting collisions between objects. The algorithm
is able to estimate upcoming criticality at very early stages, demonstrating
its potential for vehicle safety-systems and autonomous driving applications.
An implementation on an embedded system in a test vehicle proves in a
prototypical manner the compatibility of the algorithm with the hardware
possibilities of modern cars. For a complex traffic scenario with 11 dynamic
objects, more than 86 million pose combinations are evaluated in 21 ms on the
GPU of a Drive PX~2
Validate implementation correctness using simulation: the TASTE approach
High-integrity systems operate in hostile environment and must guarantee a continuous operational state, even if unexpected events happen. In addition, these systems have stringent requirements that must be validated and correctly translated from high-level specifications down to code. All these constraints make the overall development process more time-consuming. This becomes especially complex because the number of system functions keeps increasing over the years.
As a result, engineers must validate system implementation and check that its execution conforms to the specifications. To do so, a traditional approach consists in a manual instrumentation of the implementation code to trace system activity while operating. However, this might be error-prone because modifications are not automatic and still made manually. Furthermore, such modifications may have an impact on the actual behavior of the system.
In this paper, we present an approach to validate a system implementation by comparing execution against simulation. In that purpose, we adapt TASTE, a set of tools that eases system development by automating each step as much as possible. In particular, TASTE automates system implementation from functional (system functions description with their properties â period, deadline, priority, etc.) and deployment(processors, buses, devices to be used) models.
We tailored this tool-chain to create traces during system execution. Generated output shows activation time of each task, usage of communication ports (size of the queues, instant of events pushed/pulled, etc.) and other relevant execution metrics to be monitored. As a consequence, system engineers can check implementation correctness by comparing simulation and execution metrics
MLPerf Inference Benchmark
Machine-learning (ML) hardware and software system demand is burgeoning.
Driven by ML applications, the number of different ML inference systems has
exploded. Over 100 organizations are building ML inference chips, and the
systems that incorporate existing models span at least three orders of
magnitude in power consumption and five orders of magnitude in performance;
they range from embedded devices to data-center solutions. Fueling the hardware
are a dozen or more software frameworks and libraries. The myriad combinations
of ML hardware and ML software make assessing ML-system performance in an
architecture-neutral, representative, and reproducible manner challenging.
There is a clear need for industry-wide standard ML benchmarking and evaluation
criteria. MLPerf Inference answers that call. In this paper, we present our
benchmarking method for evaluating ML inference systems. Driven by more than 30
organizations as well as more than 200 ML engineers and practitioners, MLPerf
prescribes a set of rules and best practices to ensure comparability across
systems with wildly differing architectures. The first call for submissions
garnered more than 600 reproducible inference-performance measurements from 14
organizations, representing over 30 systems that showcase a wide range of
capabilities. The submissions attest to the benchmark's flexibility and
adaptability.Comment: ISCA 202
Sharing Human-Generated Observations by Integrating HMI and the Semantic Sensor Web
Current âInternet of Thingsâ concepts point to a future where connected objects gather meaningful information about their environment and share it with other objects and people. In particular, objects embedding Human Machine Interaction (HMI), such as mobile devices and, increasingly, connected vehicles, home appliances, urban interactive infrastructures, etc., may not only be conceived as sources of sensor information, but, through interaction with their users, they can also produce highly valuable context-aware human-generated observations. We believe that the great promise offered by combining and sharing all of the different sources of information available can be realized through the integration of HMI and Semantic Sensor Web technologies. This paper presents a technological framework that harmonizes two of the most influential HMI and Sensor Web initiatives: the W3Câs Multimodal Architecture and Interfaces (MMI) and the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) with its semantic extension, respectively. Although the proposed framework is general enough to be applied in a variety of connected objects integrating HMI, a particular development is presented for a connected car scenario where driversâ observations about the traffic or their environment are shared across the Semantic Sensor Web. For implementation and evaluation purposes an on-board OSGi (Open Services Gateway Initiative) architecture was built, integrating several available HMI, Sensor Web and Semantic Web technologies. A technical performance test and a conceptual validation of the scenario with potential users are reported, with results suggesting the approach is soun
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