Ratatoskr: An Open-Source Framework for In-Depth Power, Performance, and Area Analysis and Optimization in 3D NoCs

We introduce Ratatoskr , an open-source framework for in-depth power, performance, and area (PPA) analysis in Networks-on-Chips (NoCs) for 3D-integrated and heterogeneous System-on-Chips (SoCs). It covers all layers of abstraction by providing an NoC hardware implementation on Register Transfer Level (RTL), an NoC simulator on cycle-accurate level and an application model on transaction level. By this comprehensive approach, Ratatoskr can provide the following specific PPA analyses: Dynamic power of links can be measured within 2.4% accuracy of bit-level simulations while maintaining cycle-accurate simulation speed. Router power is determined from RTL-to-gate-level synthesis combined with cycle-accurate simulations. The performance of the whole NoC can be measured both via cycle-accurate and RTL simulations. The performance (i.e., timing) of individual routers and the NoC area are obtained from RTL synthesis results. Despite these manifold features, Ratatoskr offers easy two-step user interaction: (1) A single point-of-entry allows setting design parameters. (2) PPA reports are generated automatically. For both the input and the output, different levels of abstraction can be chosen for high-level rapid network analysis or low-level improvement of architectural details. The synthesizable NoC-RTL model shows improved total router power and area in comparison to a conventional standard router. As a forward-thinking and unique feature not found in other NoC PPA-measurement tools, Ratatoskr supports heterogeneous 3D integration that is one of the most promising integration paradigms for upcoming SoCs. Thereby, Ratatoskr lays the groundwork to design their communication architectures. The framework is publicly available at https://github.com/ratatoskr-project .

The Six Components of Social Interactions: Actor, Partner, Relation, Activities, Context, and Evaluation

Social interactions are essential aspects of social relationships. Despite their centrality, there is a lack of a standardized approach to systematize social interactions. The present research developed (Study 1) and tested (Study 2) a taxonomy of social interactions. In Study 1 (5,676 descriptions of social interactions from N = 708 participants, age range 18–83 years), we combined a bottom-up approach based on the grounded theory with a top-down approach integrating existing empirical and theoretical literature to develop the taxonomy. The resulting taxonomy (APRACE) comprises the components Actor, Partner, Relation, Activities, Context, and Evaluation, each specified by features on three levels of abstraction. A social situation can be described by a combination of the components and their features on the respective abstraction level. Study 2 tested the APRACE using another dataset (N = 303, age range 18–88 years) with 1,899 descriptions of social interactions. The index scores of the six components, the frequencies of the features on the most abstract level, and their correlations were largely consistent across both studies, which supports the generalizability of the APRACE. The APRACE offers a generalizable tool for the comprehensive, parsimonious, and systematic description of social interactions and, thus, enables networked research on social interactions and application in a number of practical fields.

Is Passive Priming Really Impervious to Verb Semantics? A High-Powered Replication of Messenger Et al. (2012)

The aim of the present study was to conduct a particularly stringent pre-registered in-vestigation of the claim that there exists a level of linguistic representation that “includes syntactic category information but not semantic information” (Branigan & Pickering, 2017: 8). As a test case, we focussed on the English passive; a construction for which previous findings have been somewhat contradictory. On the one hand, several studies using different methodologies have found an advantage for theme-experiencer passives (e.g., The girl was shocked by the tiger; and also agent-patient passives; e.g., The girl was hit by the tiger) over experiencer-theme passives (e.g., The girl was ignored by the tiger). On the other hand, Messenger et al. (2012) found no evidence that theme-experiencer and experiencer-theme passives vary in their propensity to prime production of agent-patient passives. We therefore conducted an online replication of Messen-ger et al (2012) with a pre-registered appropriately powered sample (N=240). Although a large and significant priming effect (i.e., an effect of prime sentence type) was ob-served, a Bayesian analysis yielded only weak/anecdotal evidence (BF=2.11) for the crucial interaction of verb type by prime type; a finding that was robust to different coding and exclusion decisions, operationalizations of verb semantics (dichoto-mous/continuous), analysis frameworks (Bayesian/frequentist) and – as per a mixed-effects-multiverse analyses – random effects structures. Nevertheless, these findings do no not provide evidence for the absence of semantic effects (as has been argued for the findings of Messenger et al, 2012). We conclude that these and related findings are best explained by a model that includes both lexical, exemplar-level representations and rep-resentations at multiple higher levels of abstraction.

Foundations of the Socio-Physical Model of Activities (SOMA) for Autonomous Robotic Agents1

In this paper, we present foundations of the Socio-physical Model of Activities (SOMA). SOMA represents both the physical as well as the social context of everyday activities. Such tasks seem to be trivial for humans, however, they pose severe problems for artificial agents. For starters, a natural language command requesting something will leave many pieces of information necessary for performing the task unspecified. Humans can solve such problems fast as we reduce the search space by recourse to prior knowledge such as a connected collection of plans that describe how certain goals can be achieved at various levels of abstraction. Rather than enumerating fine-grained physical contexts SOMA sets out to include socially constructed knowledge about the functions of actions to achieve a variety of goals or the roles objects can play in a given situation. As the human cognition system is capable of generalizing experiences into abstract knowledge pieces applicable to novel situations, we argue that both physical and social context need be modeled to tackle these challenges in a general manner. The central contribution of this work, therefore, lies in a comprehensive model connecting physical and social entities, that enables flexibility of executions by the robotic agents via symbolic reasoning with the model. This is, by and large, facilitated by the link between the physical and social context in SOMA where relationships are established between occurrences and generalizations of them, which has been demonstrated in several use cases in the domain of everyday activites that validate SOMA.

Testing the Principle of No Synonymy across levels of abstraction

This corpus-based study tests the Principle of No Synonymy across levels of abstraction by examining the syntactic realizations of subject extraposition (e.g., it is important to, it seems that), and by investigating at which level(s) of formal description a difference in form also entails a difference in function. The results show that distinct pairs of form and function, i.e. constructions, can be found at different levels of abstraction, but that these constructions also subsume formal realization patterns that do not encode a difference in function. This suggests that the Principle of No Synonymy largely breaks down at low levels of formal description. The study also offers a constructional account of subject extraposition by identifying a number of subject extraposition constructions, thereby showing that this is a syntactic phenomenon that is best analyzed as a family of constructions.

Categorical working memory codes in human visual cortex

Working memory contents are represented in neural activity patterns across multiple regions of the cortical hierarchy. It has remained unclear to which degree this reflects a specialization for different levels of abstraction. Here, we demonstrate that for color stimuli categorical codes are already present at the level of extrastriate visual cortex (V4 and VO1). Importantly, this categorical coding was observed during working memory, but not during perception.

The characterization of actions at the superordinate, basic and subordinate level

Objects can be categorized at different levels of abstraction, ranging from the superordinate (e.g., fruit) and the basic (e.g., apple) to the subordinate level (e.g., golden delicious). The basic level is assumed to play a key role in categorization, e.g., in terms of the number of features used to describe these actions and the speed of processing. To which degree do these principles also apply to the categorization of observed actions? To address this question, we first selected a range of actions at the superordinate (e.g., locomotion), basic (e.g., to swim) and subordinate level (e.g., to swim breaststroke), using verbal material (Experiments 1–3). Experiments 4–6 aimed to determine the characteristics of these actions across the three taxonomic levels. Using a feature listing paradigm (Experiment 4), we determined the number of features that were provided by at least six out of twenty participants (common features), separately for the three different levels. In addition, we examined the number of shared (i.e., provided for more than one category) and distinct (i.e., provided for one category only) features. Participants produced the highest number of common features for actions at the basic level. Actions at the subordinate level shared more features with other actions at the same level than those at the superordinate level. Actions at the superordinate and basic level were described with more distinct features compared to those provided at the subordinate level. Using an auditory priming paradigm (Experiment 5), we observed that participants responded faster to action images preceded by a matching auditory cue corresponding to the basic and subordinate level, but not for superordinate level cues, suggesting that the basic level is the most abstract level at which verbal cues facilitate the processing of an upcoming action. Using a category verification task (Experiment 6), we found that participants were faster and more accurate to verify action categories (depicted as images) at the basic and subordinate level in comparison to the superordinate level. Together, in line with the object categorization literature, our results suggest that information about action categories is maximized at the basic level.

Functional Model of a Self-Driving Car Control System

The article describes a structural and functional model of a self-driving car control system, which generates a wide class of mathematical problems. Currently, control systems for self-driving cars are considered at several levels of abstraction and implementation: Mechanics, electronics, perception, scene recognition, control, security, integration of all subsystems into a solid system. Modern research often considers particular problems to be solved for each of the levels separately. In this paper, a parameterized model of the integration of individual components into a complex control system for a self-driving car is considered. Such a model simplifies the design and development of self-driving control systems with configurable automation tools, taking into account the specifics of the solving problem. The parameterized model can be used for CAD design in the field of self-driving car development. A full cycle of development of a control system for a self-driving truck was implemented, which was rub in the “Robocross 2021” competition. The software solution was tested on more than 40 launches of a self-driving truck. Parameterization made it possible to speed up the development of the control system, expressed in man-hours, by 1.5 times compared to the experience of the authors of the article who participated in the same competition in 2018 and 2019. The proposed parameterization was used in the development of individual CAD elements described in this article. Additionally, the implementation of specific modules and functions is a field for experimental research.

Kimera: From SLAM to spatial perception with 3D dynamic scene graphs

Humans are able to form a complex mental model of the environment they move in. This mental model captures geometric and semantic aspects of the scene, describes the environment at multiple levels of abstractions (e.g., objects, rooms, buildings), includes static and dynamic entities and their relations (e.g., a person is in a room at a given time). In contrast, current robots’ internal representations still provide a partial and fragmented understanding of the environment, either in the form of a sparse or dense set of geometric primitives (e.g., points, lines, planes, and voxels), or as a collection of objects. This article attempts to reduce the gap between robot and human perception by introducing a novel representation, a 3D dynamic scene graph (DSG), that seamlessly captures metric and semantic aspects of a dynamic environment. A DSG is a layered graph where nodes represent spatial concepts at different levels of abstraction, and edges represent spatiotemporal relations among nodes. Our second contribution is Kimera, the first fully automatic method to build a DSG from visual–inertial data. Kimera includes accurate algorithms for visual–inertial simultaneous localization and mapping (SLAM), metric–semantic 3D reconstruction, object localization, human pose and shape estimation, and scene parsing. Our third contribution is a comprehensive evaluation of Kimera in real-life datasets and photo-realistic simulations, including a newly released dataset, uHumans2, which simulates a collection of crowded indoor and outdoor scenes. Our evaluation shows that Kimera achieves competitive performance in visual–inertial SLAM, estimates an accurate 3D metric–semantic mesh model in real-time, and builds a DSG of a complex indoor environment with tens of objects and humans in minutes. Our final contribution is to showcase how to use a DSG for real-time hierarchical semantic path-planning. The core modules in Kimera have been released open source.

Towards an Ontology-driven Intellectual Properties reuse for Systems On Chip design

Intellectual Properties reuse has gained widespread acceptance in System-On-Chip design to manage the complexity and shorten the time-to-market. However the need for a standard representation that permits IPs classification, characterization, and integration is still a big challenge. To address this problem, we propose to develop an IPs reuse specific ontology that facilitates IPs reuse at many levels of abstraction and independently from any design language or tool. Our ontology is built using the Protégé-OWL tool