HiPEAC’s Tech Transfer Awards recognize successful examples of technology transfer, which covers technology licensing, providing dedicated services or creating a new company, for example.
For the purposes of the awards, technology transfer is defined as a contractually documented joint- or privately funded academia-industry project or technology licence agreement, with the goal of bringing a concrete research result into industrial practice. All applications are evaluated by an internal technology transfer committee.
This year, nine winners have been selected, as follows:
José M. Cecilia, Universidad Católica de Murcia (UCAM): Multisensor data fusion strategies for the prevention of water-related disasters in El Salvador
Due to factors such as increased event frequency and magnitude, unplanned urbanization, insecure livelihoods and inaccurate public perception of risk, the impact and costs of water-related disasters are rising, particularly in developing countries.
In this contract between the company Vielca Ingenieros, the Environment Ministry of El Salvador, the Universidad Católica de Murcia (UCAM) and the Universitat Politècnica de València, a real-time, context-aware application for the early detection and prevention of water-related disasters in the Salvador area was developed. Created in this project from scratch, the technology developed consists of a big-data analytics tool that combines information from ‘hard’ sensors – information from meteorological stations – and ‘soft’ sensors – microtexts from social networks, analysed using natural language processing techniques.
Francesco Conti, University of Bologna: Ultra-Low Energy Hardware Convolution Engine for GAP-8 IoT Application Processor
The hardware convolution engine is state-of-the-art hardware acceleration intellectual property to boost performance and energy on convolutional neural networks, the leading class of deep learning algorithms. In particular, it is designed to facilitate the deployment of convolutional neural networks on low-footprint, ultra-low-power endnodes for the IoT.
Based on an extension of the PULP platform, the hardware convolution engine was licensed to the French fabless company GreenWaves Technologies in 2017 for integration in their GAP8 IoT application processor.
George Dan Mois, Technical University of Cluj-Napoca: Thermal Printer, Bluetooth Low Energy and microSD Data Logger
Funded by the Romanian government, this project involved the design of a temperature and relative humidity data logger. Data is stored on a high-capacity memory device and can be printed and / or downloaded using Bluetooth Low Energy technology.
Synchro, the company involved, specializes in the development of industrial measurement and control equipment. A prototype produced as part of the project is currently being tested and will be launched in due course.
Bjorn De Sutter, Ghent University: Tightly-coupled self-debugging software protection
A tightly coupled self-debugging technique was developed at Ghent University to overcome the pitfalls of current anti-debugging techniques, which aim to protect software against reverse engineering. The technique makes it harder for attackers to reverse-engineer a program or reconvert it into the original, unprotected program.
This technology, including software architecture and compiler technology, was transferred to the digital security company Nagravision.
Oscar Deniz Suarez, University of Castilla-La Mancha (UCLM): Eyes of Things
The technology transferred is the Eyes of Things (EoT) platform, which received funding from the European Commission’s Horizon 2020 programme. A reference hardware / software platform for embedded computer vision, EoT is the first proof of concept that advanced vision and deep learning inference can be embedded in a small form-factor – either as a standalone device or embedded in other artefacts.
VISILAN, the computer vision research lab at UCLM, managed the exploitation aspects, in collaboration with Intel Movidius. The company involved is Irish computer vision company Ubotica, who have acquired the necessary licences for commercial development.
Magnus Jahre, Norwegian University of Science and Technology: Non-Intrusive Power Monitoring for Embedded Systems
High-performance computing systems require a high degree of energy efficiency, which requires accurate measurement of energy consumption. As part of the European Union-funded TULIPP project, the Norwegian University of Science and Technology (NTNU) developed Lynsyn, a low-cost, external measurement device that that helps unobtrusively monitor energy consumption.
NTNU has licensed the Lynsyn power measurement device to the company Sundance Microprocessor Technologies, which will manufacture and sell Lynsyn devices.
Diego R. Llanos, University of Valladolid: RDNest, new start-up company in the field of IoT in Valladolid, Spain
RDNest was founded in 2016 to develop hardware and software for industry 4.0 and internet of things (IoT) applications, including the commercialization of the XtremeLoc indoor positioning system developed by the MoBiVAP research group at the University of Valladolid. The company, which was created with the participation of the University of Valladolid’s ‘Parque Científico’, provides opportunities for local engineering students who might otherwise leave the city.
Alessandro Pellegrini, Sapienza University of Rome: Transparent HPC Simulation on Heterogeneous Distributed Architectures
ROOT-Sim is an open-source simulation runtime environment which has been developed by Sapienza University of Rome and the University of Rome ‘Tor Vergata’ since 1987. Initially devised to deliver innovative synchronization schemes for distributed high-performance computing (HPC), research over the years has focused on different aspects, with a current emphasis on programmability and transparency.
Lockless, the company involved in the technology transfer, is a startup founded in 2018 by members of the two universities, whose mission is to democratize access to large-scale HPC platforms through simple programming models. The company is currently looking for pre-seed finance to fund the next phase of its simulation platform based on ROOT-Sim.
Spela Stres, Jožef Stefan Institute: A technology radiation dosage manipulation and surveillance
The Department for Reactor Physics at Jožef Stefan Institute developed a technology for the determination of neutron and gamma radiation object resistance. With the help of the research institute’s Center for Technology Transfer and Innovation, agreements were negotiated with the Slovenian companies Nanocut and Dito, both involved in the production of radiation-resistant equipment for nuclear facilities. The technology was also transferred to the French research and development organization CEA Cadarache in 2017 and to the French group Thermocoax in 2018.