Current Projects

Research Projects at the Institute

Digital Modeling of Asynchronous Integrated Circuits

Funding: Austrian Science Fund (FWF)

Collaborators: Matthias Függer (ENS Paris-Saclay),Thomas Nowak (Université Paris-Sud), Sayan Mitra (U. Illinois at Urbana-Champaign), Laura Nenzi (U. of Trieste), Ezio Bartocci (TU Wien)

Time Frame: started 01. 07. 2019

Contact Persons: Ulrich Schmid

Research Team: Ulrich Schmid, Jürgen Maier

The project Digital Modeling of Asynchronous Integrated Circuits (DMAC) is devoted to the development of a purely digital model for asynchronous circuits, which enables accurate and fast dynamic timing analysis and is a mandatory prerequisite for any attempt on practical formal verification of such designs. The envisioned model shall be accurate and realistic (= faithful), in the sense that the behavior of circuits described in the model is exactly, i.e., within the modeling accuracy, the same as the behavior of the corresponding real circuit. In contrast to analog models, which are known to be faithful but suffer from excessive simulation times, we target continuous-time discrete-value models here, which essentially boil down to elaborate delay models for gates and/or interconnecting channels.

The project builds on our novel involution model, which considers input-to-output delays as as function of the history in the signal trace. DMAC will fully explore this avenue scientifically, by answering questions such as how to enlarge the class of circuits where the involution model and variants thereof are faithful, how to compose gates with different electrical properties, in particular, different threshold voltage levels, how to accurately model subtle delay variation originating in the Charlie effect in multi-input gates, how to parametrize and characterize the model for a given technology and given operating conditions, and how to possibly further improve the modeling coverage and accuracy. In addition, we will also make the first steps to incorporate our models into existing timing analysis and verification tools for validation purposes.

Some key publications:

Matthias Függer, Thomas Nowak, and Ulrich Schmid. Unfaithful glitch propagation in existing binary circuit models. IEEE Transactions on Computers, 65(3):964–978, March 2016. (doi:10.1109/TC.2015.2435791)

Matthias Függer, Robert Najvirt, Thomas Nowak, and Ulrich Schmid. A Faithful Binary Circuit Model. IEEE Transactions on Computer-Aided Design, 2019. (doi:10.1109/TCAD.2019.2937748)


Gracefully Degrading Agreement in Directed Dynamic Networks

Funding: Austrian Science Fund (FWF)

Time Frame: started 01. 01. 2016

Contact Persons: Ulrich Schmid

Research Team: Ulrich Schmid, Manfred Schwarz, Kyrill Winkler

This project is devoted to the development of the theoretical foundations, models, algorithms and analysis techniques for relaxed distributed agreement in directed dynamic networks. Such networks are characterized by (i) sets of participants (processes) that are a priori unknown and potentially time-varying, (ii) rapidly changing uni-directional connectivity between processes, and (iii) the absence of central control. Instantiated, e.g., by (wireless) sensor networks and ad-hoc networks, such dynamic networks are becoming ubiquitous in many applications nowadays. A natural approach to build robust services despite the dynamic nature of such networks would be to use distributed consensus to agree system-wide on (fundamental) parameters like schedules, operating frequencies, operating modes etc. Unfortunately, however, in larger-scale dynamic networks, this is usually impossible, since solving consensus requires a well-connected and temporarily stable network topology. In order to overcome this fundamental limitation, we propose to consider gracefully degrading variants of consensus, in particular, approximate agreement, where decision values may slightly deviate from each other, and k-set agreement, which may deliver up to k different decisions in case of bad network conditions that e.g. lead to k isolated network partitions. In our project, we will develop network assumptions that both allow to solve, say, k-set agreement, and have some reasonable assumption coverage in real systems. Therefore, our focus will be on weakest (necessary and sufficient) conditions and the analysis of the resulting assumption coverage. Other central part of our project is the development of solution algorithms and their correctness proofs. Particular emphasis will be put on performance of our algorithms, since there is a tradeoff between weak network conditions and the communication and memory complexity of solutions algorithms. Overall, the project shall yield new insights into the fundamental limitations of dynamic networks as well as the development of novel algorithms that solve distributed agreement problems reliably even under very weak communication guarantees.


IoT4CPS: Trustworthy IoT for CPS

IoT4CPS: Trustworthy IoT for CPS

Funding: AT-FFG

Partners: TU Wien, Austrian Institut of Technology (AIT), Institute of Science and Technology (IST), AVL List GmbH, Donau Uni Krems, Infineon Technologies AG, JKU Linz, Joanneum, NOKIA Österreich, NXP, Salzburg Research, SBA Research, SCCH, Siemens AG Österreich, TTTech Computertechnik AG, TU Graz, X-Net Services GmbH

Time Frame: started 01. 12. 2017

Contact Persons: Radu Grosu, Muhammad Shafique

Research Team: Radu Grosu, Muhammad Shafique

IoT4CPS will develop guidelines, methods and tools to enable safe and secure IoT-based applications for automated driving and for smart production. The project will address safety and security aspects in a holistic approach both along the specific value chains and the product life cycles. To ensure the outreach of the project activities and results, the relevant stakeholders will be involved throughout the project and results will be disseminated to expert groups and standardization bodies. IoT4CPS will support digitalization along the entire product lifecycle, leading to a time-to-market acceleration for connected and autonomous vehicles. IoT4CPS will provide innovative components, leading to efficiency increases for the deployment of autonomous driving functions and in smart production environments, which will be validated in a vehicle and in a smart production demonstrator.