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Obtaining statistical data

Projects / Programmes source: ARRS

Computer Structures and Systems

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Periods
January 1, 2019 - December 31, 2024
Research activity

Code Science Field Subfield
2.07.00  Engineering sciences and technologies  Computer science and informatics   
1.01.00  Natural sciences and mathematics  Mathematics   

Code Science Field
T120  Technological sciences  Systems engineering, computer technology 

Code Science Field
1.02  Natural Sciences  Computer and information sciences 
1.01  Natural Sciences  Mathematics 
Keywords
Reconfigurable computer structures, reconfigurable optimization algorithms, context-awareness, applied statistical analysis, network topology, reconfigurable hardware platforms, hardware self-correction, approximate computing
Evaluation (rules)
source: COBISS
Points
9,455.61
A''
1,875.58
A'
4,827.09
A1/2
5,999.97
CI10
3,951
CImax
187
h10
30
A1
32.95
A3
22.66
Data for the last 5 years (citations for the last 10 years) on June 1, 2023; A3 for period 2017-2021
Data for ARRS tenders ( 04.04.2019 – Programme tender , archive )
Citations Citations for bibliographic records in COBIB.SI that are linked to records in citation databases
source: WoS source: Scopus source: COBISS
Database Linked records Citations Pure citations Average pure citations
WoS  356  3,219  2,701  7.59 
Scopus  533  5,434  4,495  8.43 
Researchers (25)
no. Code Name and surname Research area Role Period No. of publications
1.  51347  Margarita Antoniou    Technician  2020 - 2023  19 
2.  11983  PhD Anton Biasizzo  Engineering sciences and technologies  Researcher  2019 - 2023  147 
3.  33582  PhD Bojan Blažica  Engineering sciences and technologies  Researcher  2019 - 2023  58 
4.  57085  Gjorgjina Cenikj  Engineering sciences and technologies  Junior researcher  2022 - 2023  25 
5.  50854  PhD Tome Eftimov  Engineering sciences and technologies  Researcher  2019 - 2023  212 
6.  39138  Rok Hribar  Engineering sciences and technologies  Technician  2019 - 2023  19 
7.  52408  PhD Gordana Ispirova  Engineering sciences and technologies  Technician  2019 - 2023  39 
8.  22314  PhD Peter Korošec  Engineering sciences and technologies  Researcher  2019 - 2023  229 
9.  10824  PhD Barbara Koroušić Seljak  Engineering sciences and technologies  Researcher  2019 - 2023  321 
10.  54881  Robert Modic    Technician  2022 - 2023  12 
11.  05601  PhD Franc Novak  Engineering sciences and technologies    2019 - 2020  317 
12.  53869  Matevž Ogrinc    Technician  2022 - 2023  18 
13.  18291  PhD Gregor Papa  Engineering sciences and technologies  Principal Researcher  2019 - 2023  349 
14.  16034  PhD Marko Pavlin  Engineering sciences and technologies  Researcher  2019 - 2023  110 
15.  37955  PhD Veljko Pejović  Engineering sciences and technologies  Researcher  2019 - 2023  126 
16.  54581  Gašper Petelin  Engineering sciences and technologies  Junior researcher  2021 - 2023  23 
17.  54702  Gorjan Popovski  Engineering sciences and technologies  Junior researcher  2020 - 2021  27 
18.  04378  PhD Marina Santo Zarnik  Engineering sciences and technologies  Researcher  2019 - 2023  374 
19.  56050  Andraž Simčič    Technician  2022 - 2023  9 
20.  09862  PhD Jurij Šilc  Engineering sciences and technologies    2019 - 2023  359 
21.  51348  PhD Urban Škvorc  Engineering sciences and technologies  Junior researcher  2019 - 2022  16 
22.  11972  PhD Drago Torkar  Engineering sciences and technologies  Researcher  2019 - 2023  91 
23.  52209  Eva Valenčič  Engineering sciences and technologies  Technician  2019 - 2023  26 
24.  55132  Jure Vreča  Engineering sciences and technologies  Technician  2021 - 2023  6 
25.  30891  PhD Vida Vukašinović  Engineering sciences and technologies  Researcher  2019 - 2023  58 
Organisations (1)
no. Code Research organisation City Registration number No. of publications
1.  0106  Jožef Stefan Institute  Ljubljana  5051606000  85,799 
Abstract
The importance and complexity of computer systems are ever increasing. The combination of customizable computer hardware and efficient algorithms for processing complex-data is the basis for reconfigurable computer systems that are able to change their structure and their function in response to external and/or internal stimuli. Reconfigurable structures provide the means to develop advanced computer systems that can function, to a large extent, autonomously without human intervention and have the ability to correct data, as well as to adapt and repair themselves. They are distributed, scalable, resilient, predictive and intelligent. They can handle data-intensive requirements, can process complex massive data, and have low-latency in data processing. To be able to do all this they require increased performance and lower power consumption. The scientific background of the Computer Structures and Systems research programme addresses both these issues and is based on advanced algorithm engineering and adaptive computing hardware. The research Programme is designed to align with European and national roadmaps and strategic papers: the HiPEAC Vision 2017, the ARTEMIS strategic research agenda, and Slovenia's Smart Specialisation Strategy. These documents foresee relevant research and development in areas strongly related to reconfigurable systems: dependability, architectures for data-intensive systems, hardware/software co-design, resource planning and scheduling to allow for energy efficiency, code scalability, adaptive and learning control methodologies, dynamic adaptation to changing contexts, decision and control in uncertain and changing contexts. The existence of complex massive data in real-life processing means that reconfigurable computer structures require new and innovative approaches to run and manage the processes. As a consequence, such (usually embedded) structures must be customizable and adaptable to changing operational contexts, environments or system characteristics, while ensuring resilience, energy efficiency and recoverability. The interdisciplinary state-of-the-art research challenges combine fields from computer science and mathematics: Reconfigurable optimisation algorithms (to efficiently deal with massive data in dynamic and uncertain environments), based on context-awareness (to decide when to reconfigure), with the support of applied statistical analysis and network topology (to determine how to reconfigure). They are implemented by reconfigurable hardware platforms (based on intrinsically parallelised FPGAs), that ensure self-correction (for structure reliability) and allow for approximate computing (for energy efficiency).
Significance for science
The relevance of the research results of the Computer Structures and Systems research programme is demonstrated by very good correlation between its research objectives and those of the EU Framework Programme for Research and Innovation Horizon 2020. The Programme is also in accordance with Slovenian Research and Innovation Strategy, Slovenia's Smart Specialisation Strategy and the mission of the Slovenian Research Agency. The results of the research will be efficient and reconfigurable computer structures that support the development of the most advanced computing systems. These structures will be used in different real-world applications, like production/manufacturing, infrastructure (transport, energy distribution), health-care, and medicine, with the aim being to ensure social and technological progress. The Programme’s research directions represent hot topics, but they nevertheless require some additional critical reflection. From our experience we know that software solutions enhanced by a hardware implementation are not always optimal by default. Considerable effort and in-depth knowledge of the target architecture is imperative for an efficient solution, especially when considering edge computing. To develop reconfigurable computing structures in the scope of the Programme requires in-depth theoretical knowledge of reconfigurable/adaptive algorithm approaches and reconfigurable hardware platforms. Our specific knowledge of metaheuristic algorithms, expertise in reconfigurable-systems programming, machine learning, complex network topology and statistical analysis will ensure that the proposed research will lead to effective solutions, which will make a valuable contribution to the advancement of computer science. Our research will also make a general contribution to the development of science and the profession by stimulating current research and fostering novel research directions. The Programme will expand the research in several scientific fields, and will play a role in collaborative workshops and seminars for students, researchers and companies. New research directions will also be covered and maintained by several additional research activities (through the national Young Researcher Programme and international actions such as Marie Skłodowska-Curie Actions, ERA Chairs, ERC projects, COST Actions).
Significance for the country
The importance of our research for Slovenia's socio-economic and cultural development will be in substantial contributions to novel research directions in reconfigurable computing structures and their effective implementations in real-world applications. As already in the past, we will exploit our solutions to various products, technologies and innovations. Our solutions of reconfigurable optimisation algorithms are expected to be used in transportation systems that are implicitly dynamic. Nowadays, multimodal transportation and logistics requirements demand the on-line adaptation of schedules and plans to allow fluent transportation and just-in-time deliveries. Our knowledge of multi-objective optimisation, parallelisation and surrogate modelling, as a result of our Horizon 2020 Twinning SYNERGY project, will further accelerate the emergence of solutions. In addition, reconfigurable optimisation is foreseen in the pharmaceutical and health domains, where enormous amounts of data are being collected, while striving to find proper new drugs, through the use of deep neural networks, that are able to effectively treat different diseases. Deep learning and convolutional neural networks are finding new uses every day. It is expected that applications supported by deep learning will be used in several areas, from face- and food-image recognition applications in smartphones to the modelling of complex technological processes in industry. Similarly, biomedical signal processing supported by machine learning has great potential in healthcare applications, life-signs monitoring systems, heartbeat-monitoring apps, clinical studies of new drugs, ECG and EEG signal analysis, early detection of diseases, etc. The use of our reconfigurable hardware platforms and at the support of our TETRAMAX Competence centre in customized low-energy computation (CLEC), will enable interested Slovenian SMEs to gain additional knowledge to develop their own solutions with low-latency and low-energy demands.
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