Toward Contention Analysis for Parallel Executing Real-Time Tasks

In measurement-based probabilistic timing analysis, the execution conditions imposed to tasks as measurement scenarios, have a strong impact to the worst-case execution time estimates. The scenarios and their effects on the task execution behavior have to be deeply investigated. The aim has to be to identify and to guarantee the scenarios that lead to the maximum measurements, i.e. the worst-case scenarios, and use them to assure the worst-case execution time estimates. We propose a contention analysis in order to identify the worst contentions that a task can suffer from concurrent executions. The work focuses on the interferences on shared resources (cache memories and memory buses) from parallel executions in multi-core real-time systems. Our approach consists of searching for possible task contenders for parallel executions, modeling their contentiousness, and classifying the measurement scenarios accordingly. We identify the most contentious ones and their worst-case effects on task execution times. The measurement-based probabilistic timing analysis is then used to verify the analysis proposed, qualify the scenarios with contentiousness, and compare them. A parallel execution simulator for multi-core real-time system is developed and used for validating our framework. The framework applies heuristics and assumptions that simplify the system behavior. It represents a first step for developing a complete approach which would be able to guarantee the worst-case behavior

NasHD: Efficient ViT Architecture Performance Ranking using Hyperdimensional Computing

Neural Architecture Search (NAS) is an automated architecture engineering method for deep learning design automation, which serves as an alternative to the manual and error-prone process of model development, selection, evaluation and performance estimation. However, one major obstacle of NAS is the extremely demanding computation resource requirements and time-consuming iterations particularly when the dataset scales. In this paper, targeting at the emerging vision transformer (ViT), we present NasHD, a hyperdimensional computing based supervised learning model to rank the performance given the architectures and configurations. Different from other learning based methods, NasHD is faster thanks to the high parallel processing of HDC architecture. We also evaluated two HDC encoding schemes: Gram-based and Record-based of NasHD on their performance and efficiency. On the VIMER-UFO benchmark dataset of 8 applications from a diverse range of domains, NasHD Record can rank the performance of nearly 100K vision transformer models with about 1 minute while still achieving comparable results with sophisticated models

Prior knowledge contribution to declarative learning. A study in amnesia, aging and Alzheimer's disease

L'étude expérimentale de la mémoire humaine a connu deux moments historiques dans les soixante dernières années. 1957 marque la découverte du rôle du lobe temporal interne bilatéral dans l'apprentissage conscient, déclaratif. 1997 marque la découverte de deux systèmes de mémoire déclarative, épisodique et sémantique. Ces découvertes résultent d'études de cas en neuropsychologie. Cette thèse s'inscrit dans la tradition neuropsychologique: sa genèse doit tout à un patient souffrant d'une forme atypique d'amnésie développementale, le patient KA. Son point de départ est une étude de cas approfondie, avec deux résultats surprenants. Malgré une amnésie sévère, KA dispose de connaissances sémantiques exceptionnelles. Par ailleurs, il montre des capacités préservées d'apprentissage explicite, mais uniquement pour des stimuli concrets, pas abstraits. En conséquence, cette thèse a exploré deux pistes de recherche. Premièrement, nous avons caractérisé les processus préservés d'apprentissage déclaratif et l'anatomie cérébrale chez ce patient. Deuxièmement, nous avons étudié le rôle des connaissances préalables dans l'apprentissage: comment ce que l'on sait influence ce dont nous nous souvenons ? Une première série d'expériences montre chez ce patient une atteinte sévère et sélective de l'ensemble du système hippocampique, alors que les structures sous- hippocampiques (cortex entorhinal, périrhinal et parahippocampique) sont préservées. Malgré une amnésie épisodique sévère, nous montrons des connaissances sémantiques supranormales et des aptitudes d'apprentissage explicite rapide. Ces aptitudes sont toutefois restreintes aux stimuli associés à des connaissances préalables. Une seconde série d'expériences explore l'hypothèse selon laquelle les connaissances préalables facilitent l'apprentissage en mémoire déclarative, même dans les situations où le lobe temporal interne est fragilisé, comme dans le vieillissement, ou lésé, comme chez le patient KA ou dans la maladie d'Alzheimer. Nos résultats suggèrent l'existence de processus d'apprentissage rapide en mémoire déclarative, indépendants du système hippocampique et sensibles à la présence de représentations préexistantes. Ces processus semblent affectés par la maladie d'Alzheimer, et ce en lien avec un défaut d'activité des régions sous-hippocampiques antérieures. A l'inverse, les sujets âgés sains peuvent utiliser les connaissances préalables et pourraient ainsi compenser le déclin de la mémoire associative. Ce travail s'accorde avec les modèles postulant une dissociation fonctionnelle au sein du lobe temporal interne pour l'apprentissage déclaratif. Il soutient les propositions neurocognitives et computationnelles récentes, suggérant une voie d'apprentissage néocortical rapide mobilisable dans certaines circonstances. Il met en exergue la dynamique des apprentissages en mémoire déclarative et notamment l'intrication fondamentale entre "savoir" et "se souvenir". Ce que je sais a un impact profond sur ce dont je vais me souvenir. Cette thèse permet d'envisager de nouveaux outils cognitifs pour le diagnostic de la maladie d'Alzheimer. De plus, il semble que des lésions temporales internes auront un impact distinct sur l'apprentissage selon le statut des informations à mémoriser en mémoire à long terme, offrant un regard nouveau sur les effets stimulus-dépendants dans l'amnésie. Une considération approfondie des connaissances préalables associées au contenu de nos expériences, et leur caractérisation détaillée, est requise pour affiner les modèles de la mémoire déclarative. Ces résultats apportent de nouvelles pistes de recherche quant aux circonstances épargnant l'apprentissage, notamment associatif, lors du vieillissement. Plus généralement, ils contribuent à la compréhension des déterminants d'un apprentissage réussi, en mettant l'accent sur les recouvrements entre processus de récupération et d'acquisition. Des applications potentielles en découlent dans le domaine éducatif.The experimental study of human memory has had two historic moments in the last sixty years. 1957 marks the discovery of the role of the medial temporal lobes in conscious learning. 1997 marks the discovery of two systems of declarative memory, namely episodic and semantic memories. These major breakthroughs are owed to clinical case studies in neuropsychology. This thesis follows on from the neuropsychological tradition: its genesis owes everything to a patient suffering from an atypical form of developmental amnesia, the patient KA. The starting point of this work was a thorough neuropsychological study of this patient. Two striking findings shortly arose. First, despite lifelong amnesia, KA had acquired exceptional levels of knowledge about the world. Second, remaining explicit learning abilities were restricted to meaningful, not meaningless, memoranda. As a consequence, we have investigated two research pathways in that thesis. First, we aimed at better characterizing preserved learning abilities and brain structure of the patient KA. Second, our goal was to explore how prior knowledge affects new declarative learning or, put simply, how do we learn what we know? In a first series of behavioural and neuroimaging experiments, we have shown in this patient a severe and selective damage of the whole extended hippocampal system, but preserved subhippocampal structures (entorhinal, perirhinal and parahippocampal cortex). The patient suffers from severe episodic amnesia, but we bring striking evidence for supranormal semantic knowledge as well as normal explicit learning skills. These skills were, however, restricted to familiar stimuli, that is, stimuli carrying pre-experimental knowledge. In a second series of behavioural and neuroimaging experiments, we explored the hypothesis that prior knowledge can facilitate new learning in declarative memory, even in aging or in situations where structures of the medial temporal lobe are or injured, as in amnesia or Alzheimer's disease. Our results suggest the existence of processes allowing fast learning in declarative memory, independently of the hippocampal system, and that are sensitive to the presence of pre-existing representations in long-term memory. Such learning processes appear to be selectively affected by Alzheimer's disease at the pre-dementia stage, in relation to a lack of activation of subhippocampal regions. In contrast, healthy elderly were able to rely on these learning processes to compensate for the decline in associative memory associated with aging. This work lends support to the models postulating a functional dissociation with respect to learning in declarative memory. It indeed strengthens recent neurocognitive and computational accounts that suggest a rapid neocortical learning path under certain circumstances. It highlights the dynamics of learning in declarative memory and in particular the fundamental entanglement between "knowing" and "remembering". What I know profoundly impacts what I will remember. The present thesis points towards new cognitive tools for the diagnosis of Alzheimer's disease. It further brings evidence that medial temporal lesions differentially impact learning depending on the status of the memoranda in long-term memory, which sheds a new light on material-specific effects in amnesia. Our work speaks for a thorough consideration of whether the contents of events have prior representations within long-term memory, and to further better characterize their nature if we are to better understand learning mechanisms. It also brings additional clues for a deeper understanding of how learning and memory can be preserved in aging. More generally, it contributes to a better understanding of the factors determining successful learning, with a focus on how retrieval and acquisition processes overlap during learning. Such findings have potential applications in the educational field

BDD-based supervisory control on extended finite automata

In this paper, we settle some problems that are encountered when modeling and synthesizing complex industrial systems by the supervisory control theory. First, modeling such huge systems with explicit state-transition models typically results in an intractable model. An alternative modeling approach is to use extended finite automata (EFAs), which is an augmentation of ordinary automata with variables. The main advantage of utilizing EFAs for modeling is that more compact models are obtained. The second problem concerns the ease to understand and implement the supervisor. To handle this problem, we represent the supervisor in a modular manner by extending the original EFAs by compact conditional expressions generated from the monolithic supervisor. In order to, potentially, be able to handle complex systems efficiently, the models are symbolically represented by binary decision diagrams (BDDs). All computations that are performed in this framework are based on BDD operations. The framework has been implemented in a supervisory control tool and applied to industrially relevant benchmark problems

Science and Applications Space Platform (SASP) End-to-End Data System Study

The capability of present technology and the Tracking and Data Relay Satellite System (TDRSS) to accommodate Science and Applications Space Platforms (SASP) payload user's requirements, maximum service to the user through optimization of the SASP Onboard Command and Data Management System, and the ability and availability of new technology to accommodate the evolution of SASP payloads were assessed. Key technology items identified to accommodate payloads on a SASP were onboard storage devices, multiplexers, and onboard data processors. The primary driver is the limited access to TDRSS for single access channels due to sharing with all the low Earth orbit spacecraft plus shuttle. Advantages of onboard data processing include long term storage of processed data until TRDSS is accessible, thus reducing the loss of data, eliminating large data processing tasks at the ground stations, and providing a more timely access to the data

Research summary, January 1989 - June 1990

The Research Institute for Advanced Computer Science (RIACS) was established at NASA ARC in June of 1983. RIACS is privately operated by the Universities Space Research Association (USRA), a consortium of 62 universities with graduate programs in the aerospace sciences, under a Cooperative Agreement with NASA. RIACS serves as the representative of the USRA universities at ARC. This document reports our activities and accomplishments for the period 1 Jan. 1989 - 30 Jun. 1990. The following topics are covered: learning systems, networked systems, and parallel systems

Learning with delayed reinforcement in an exploratory probabilistic logic neural network

Imperial Users onl