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 .

Upon Improving the Performance of Localized Healthcare Virtual Assistants

Virtual assistants are becoming popular in a variety of domains, responsible for automating repetitive tasks or allowing users to seamlessly access useful information. With the advances in Machine Learning and Natural Language Processing, there has been an increasing interest in applying such assistants in new areas and with new capabilities. In particular, their application in e-healthcare is becoming attractive and is driven by the need to access medically-related knowledge, as well as providing first-level assistance in an efficient manner. In such types of virtual assistants, localization is of utmost importance, since the general population (especially the aging population) is not familiar with the needed “healthcare vocabulary” to communicate facts properly; and state-of-practice proves relatively poor in performance when it comes to specialized virtual assistants for less frequently spoken languages. In this context, we present a Greek ML-based virtual assistant specifically designed to address some commonly occurring tasks in the healthcare domain, such as doctor’s appointments or distress (panic situations) management. We build on top of an existing open-source framework, discuss the necessary modifications needed to address the language-specific characteristics and evaluate various combinations of word embeddings and machine learning models to enhance the assistant’s behaviour. Results show that we are able to build an efficient Greek-speaking virtual assistant to support e-healthcare, while the NLP pipeline proposed can be applied in other (less frequently spoken) languages, without loss of generality.

BioImageIT: Open-source framework for integration of image data-management with analysis

Open science and FAIR principles have become major topics in the field of bioimaging. This is due to both new data acquisition technologies that generate large datasets, and new analysis approaches that automate data mining with high accuracy. Nevertheless, data are rarely shared and rigorously annotated because it requires a lot of manual and tedious management tasks and software packaging. We present BioImageIT, an open-source framework for integrating data management according to FAIR principles with data processing.

An open-source framework for fast-yet-accurate calculation of quantum mechanical features

We present the open-source framework kallisto that enables the efficient and robust calculation of quantum mechanical features for atoms and molecules. For a benchmark set of 49 experimental molecular polarizabilities, the predictive power of the presented method competes against second-order perturbation theory in a converged atomic-orbital basis set at a fraction of its computational costs. Robustness tests within a diverse validation set of more than 80,000 molecules show that the calculation of isotropic molecular polarizabilities has a low failure-rate of only 0.3 %. We present furthermore a generally applicable van der Waals radius model that is rooted on atomic static polarizabilites. Efficiency tests show that such radii can even be calculated for small- to medium-size proteins where the largest system (SARS-CoV-2 spike protein) has 42,539 atoms. Following the work of Domingo-Alemenara et al. [Domingo-Alemenara et al., Nat. Comm., 2019, 10, 5811], we present computational predictions for retention times for different chromatographic methods and describe how physicochemical features improve the predictive power of machine-learning models that otherwise only rely on two-dimensional features like molecular fingerprints. Additionally, we developed an internal benchmark set of experimental super-critical fluid chromatography retention times. For those methods, improvements of up to 17 % are obtained when combining molecular fingerprints with physicochemical descriptors. Shapley additive explanation values show furthermore that the physical nature of the applied features can be retained within the final machine-learning models. We generally recommend the kallisto framework as a robust, low-cost, and physically motivated featurizer for upcoming state-of-the-art machine-learning studies.

Neural networks for classification problem on tabular data

This work presents the results of using self-normalizing neural networks with automatic selection of hyperparameters, TabNet and NODE to solve the problem of tabular data classification. The method of automatic selection of hyperparameters was realised. Testing was carried out with the open source framework OpenML AutoML Benchmark. As part of the work, a comparative analysis was carried out with seven classification methods, experiments were carried out for 39 datasets with 5 methods. NODE shows the best results among the following methods and overperformed standard methods for four datasets.

ATON: An Open-Source Framework for Creating Immersive, Collaborative and Liquid Web-Apps for Cultural Heritage

The web and its recent advancements represent a great opportunity to build universal, rich, multi-user and immersive Web3D/WebXR applications targeting Cultural Heritage field—including 3D presenters, inspection tools, applied VR games, collaborative teaching tools and much more. Such opportunity although, introduces additional challenges besides common issues and limitations typically encountered in this context. The “ideal” Web3D application should be able to reach every device, automatically adapting its interface, rendering and interaction models—resulting in a single, liquid product that can be consumed on mobile devices, PCs, Museum kiosks and immersive AR/VR devices, without any installation required for final users. The open-source ATON framework is the result of research and development activities carried out during the last 5 years through national and international projects: it is designed around modern and robust web standards, open specifications and large open-source ecosystems. This paper describes the framework architecture and its components, assessed and validated through different case studies. ATON offers institutions, researchers, professionals a scalable, flexible and modular solution to craft and deploy liquid web-applications, providing novel and advanced features targeting Cultural Heritage field in terms of 3D presentation, annotation, immersive interaction and real-time collaboration.