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Explore project

Cyber-Physical Systems with Humans in the Loop

Project type: Explore Project

Cyber-Physical Systems with Humans in the Loop

Summary

Constructing cyber-physical systems with humans in the loop is important in many application areas to enable a close co-operation between humans and machines. However, there are also many challenges to overcome when constructing such systems with current software technologies and human-centered design approaches. To foster collaboration on the topic the project will study state-of-the-art and map out challenges which is important for Danish industry to address in future work.

Value Creation

Scientific value: The project will provide a better terminology and a common understanding of state-of-theart across several areas of research within DIREC and disseminate this knowledge to the scientific community.

Capacity building: The project will establish new collaboration setups within DIREC and involve master students in the activities.

Business value: The project will in workshops disseminate knowledge to Danish industry and identify cases that could be relevant areas of collaboration for DIREC with Danish Industry in future larger projects. The project will among others connect to the community involved in the Nordic IoT Center.

Participants

Project Manager

Mikkel Baun Kjærgaard

Professor

University of Southern Denmark
The Maersk Mc-Kinney Moller Institute

E: mbkj@mmmi.sdu.dk

Jan Madsen

Professor

Technical University of Denmark
DTU Compute

Peter Gorm Larsen

Professor

Aarhus University
Department of Electrical and Computer Engineering

Torkild Clemmensen

Professor

Copenhagen Business School
Department of Digitalization

Kim Guldstrand Larsen

Professor

Aalborg University
Department of Computer Science

Mahyar Tourchi Moghaddam

Assistant Professor

University of Southern Denmark
The Maersk Mc-Kinnney Moller Institute

Kategorier
Explore project

Re-Use of Robotic-data in Production through search, simulation and learning

DIREC-projekt

Re-use of Robotic data in production

through Search, Simulation and Learning

Resumé

En robotdatabase med information om tidligere robotløsninger kan spare produktionsvirksomheder for tid og penge og give mindre virksomheder mulighed for også at automatisere deres produktion. 
 
Selvom det lyder enkelt, er der flere udfordringer forbundet med at skabe en robotdatabase. Robotdata er fx komplicerede, da de består af billeder, baner, kraftvektorer, information om forskellige materialer, CAD-filer osv. 
 
Med input fra industri og internationale eksperter har dette afsluttede projekt fået en meget bedre forståelse af udfordringerne. Næste trin er at udvikle software, der giver mulighed for genbrug af robotdata. 

A robot database with information on previous robot solutions can save manufacturing companies time and money and allow for smaller-scale companies to automate their production as well. This is the conclusion of the ReRoPro project. Although it sounds simple, there are several challenges involved with creating a robot database. For example, robot data are complicated as they consist of images, trajectories, force vectors, information on different materials, CAD-files etc. With input from industry and international experts, the researchers have now gained a much better understanding of the challenges.

Next step is to apply for funding to develop software that allow for the reuse of robot data. The research project took place in a cooperation between the University of Southern Denmark, University of Copenhagen and Aalborg University with the companies Rockwool, Novo Nordisk, Nordbo Robotics and WellTec as partners.

Værdi

Projektet vil opnå værdifuld viden om, hvordan man laver en robotdatabase, der kan spare produktionsvirksomheder for tid og penge og give mindre virksomheder mulighed for at automatisere deres produktion.

Nyheder / omtale

Deltagere

Project Manager

Norbert Krüger

Professor

University of Southern Denmark
The Maersk Mc-Kinney Moller Institute

E: norbert@mmmi.sdu.dk

Aljaz Kramberger

Assistant Professor

University of Southern Denmark
The Maersk Mc-Kinney Moller Institute

Jakob Wilm

Associate Professor

University of Southern Denmark
The Maersk Mc-Kinney Moller Institute

Mikkel Baun Kjærgaard

Professor

University of Southern Denmark
The Maersk Mc-Kinney Moller Institute

Anders Lyhne Kristensen

Professor

University of Southern Denmark
The Maersk Mc-Kinney Moller Institute

Kenny Erleben

Associate Professor

University of Copenhagen
Department of Computer Science

Sune Darkner

Associate Professor

University of Copenhagen
Department of Computer Science

Thomas Dyhre Nielsen

Professor

Aalborg University
Department of Computer Science

Alvaro Torralba

Associate Professor

Aalborg University
Department of Computer Science

Partnere

Kategorier
Explore project

Accountability Privacy Preserving Computation via Blockchain

Project type: Explore Project

Accountability Privacy Preserving Computation via Blockchain

Summary

The project will investigate how to combine secure multiparty computation and blockchain techniques to obtain more efficient privacy-preserving computation with accountability. Privacy-preserving computation with accountability allows computation on private data (without compromising data privacy), while obtaining an audit trail that allows third parties to verify that the computation succeeded or to identify bad actors who tried to cheat. Applications include data analysis (e.g., in the context of discrimination detection and bench marking) and fraud detection (e.g. in the financial and insurance industries).

Value Creation

Using this kind of auditable continuous secure computation can help fight discrimination and catch unethical and fraudulent behaviour. Computations that advance these goals include aggregate statistics on salary information  to help identify and eliminate wage gaps (e.g. as seen in the case of the Boston wage gap study [4]), statistics on bids in an auction or bets on a gambling site to determine whether those bids or bets are fraudulent, and many others. Organizations would not be able to carry out such computations without the use of privacy-preserving technologies due to privacy regulations; so, secure computation is necessary here. To be useful, these secure computations crucially require authenticity and consistency of the inputs. Organizations, which will not necessarily be driven by altruism, will have several incentives to participate in these computations. First, by using secure computation to detect fraud, the participants can guard against financial loss. Second, when participants are public organizations, honest participation (which anyone can verify) will generate positive publicity.

Participants

Bernardo David

Associate Professor

IT University of Copenhagen
Department of Computer Science

E: beda@itu.dk

Sophia Yakoubov

Assistant Professor

Aarhus University
Department of Computer Science

E: sophia.yakoubov@cs.au.dk

James Chiang

PhD Student

IT University of Copenhagen
Department of Computer Science
Technical University of Denmark Department of Computer Science
(co-supervised with Alberto Lluch Lafuente)

Anne Dorte Rafn Spangsberg

Cryptographic Engineer

The Alexandra Institute

E: a.d.spangsberg@alexandra.dk

Kategorier
Explore project

Certifiable Controller Synthesis for Cyber-Physical Systems

Project type: Explore Project

Certifiable Controller Synthesis for Cyber-Physical Systems

Summary

As cyber-physical systems (CPSs) are becoming ever more ubiquitous, many of them are considered safetycritical. We want to help CPS manufacturers and regulators with establishing high levels of trust in automatically synthesized control software for safety-critical CPSs. To this end, we propose to extend the technique of formal certification towards controller synthesis: controllers are synthesized together with a safety certificate that can be verified by highly trusted theorem provers.

Value Creation

From a distant view point, our project aims to increase confidence in safety-critical CPSs that interact with individuals and the society at large. This is the main motivation for applying formal methods to the construction of CPSs. However, our project aims to give a unique spin to this. By cleverly combining the existing methods of controller synthesis, (timed automata) mode checking, and interactive theorem proving via means of certificate extraction and checking, we aim to facilitate the construction of control software for CPSs that ticks all the boxes: high efficiency, a very high level of trust in the safety of the system, and the possibility to independently audit the software. Given that CPSs have already conquered every sector of life, with the bulk of the development still ahead of us, we believe such an approach could make an important contribution towards technology that benefits the people.

Moreover, our approach aims to ease the interaction between the CPS industry and certification authorities. We believe it is an important duty of regulatory authorities to safeguard their citizens from failures of critical CPSs. Even so, regulation should not grind development to a halt. With our work, we hope to somewhat remedy this apparent conflict of interests. By providing a means to check the safety of synthesized controllers in a well-documented, reproducible, and efficient manner, we believe that the interaction between producers and certifying bodies could be sped up significantly, while increasing reliability at the same time. On top of that, controller synthesis has already been intensely studied and seems to be a rather mature technology from an academic perspective. However, it has barely set a foot into industrial applications. We are confident that formal certificate extraction and checking can be an important stepping stone to help controller synthesis make this jump.

This project also contributes to the objective of DIREC to bring new academic partners together in the Danish eco-system. The two principal investigators have their specialization background in two different fields (certification theory and control theory) and have not collaborated before. Thus the project strengthens the collaboration between the two fields as well as the collaboration between the two research groups at AU and AAU. This creates the opportunity for the creation of new scientific results benefiting both research fields.

Finally, we plan to generate tangible value for industry. There are many present-day use cases for control software of critical CPSs. During our project, we want to aid these use cases with controllers that tick all of the aforementioned “boxes”. This can be done by initiating several student projects and theses supporting theory development, tool implementation, and use case demonstration. The Problem Based Learning approach of Aalborg University facilitates this greatly. Furthermore, those students can use their experience
in future positions after graduating.

Participants

Martijn Goorden

Postdoc

Aalborg University
Department of Computer Science

E: mgoorden@cs.aau.dk

Simon Wimmer

Postdoc

Aarhus University
Department of Computer Science

E: swimmer@cs.au.dk

Kategorier
Explore project

Methodologies for scheduling and routing droplets in digital microfluidic biochips

Project type: Explore Project

Methodologies for scheduling and routing droplets in digital microfluidic biochips

Summary
The overall purpose of this project is to define, investigate, and provide preliminary methodologies for scheduling and routing microliter-sized liquid droplets on a planar surface in the context of digital microfluidics. The main idea is to use a holistic approach in the design of scheduling and routing methodologies that takes into account real-world physical, topological, and behavioral constraints. Thus, producing solutions that can immediately find use in practical applications.
Value Creation
DMF biochips have been in the research spotlight for over a decade. However, the technology is still not mature at a level where it can deliver extensive automation to be used in applied biochemistry processes or for research purposes. One of the main reasons is that, although rather simple in construction, DMF biochips lack a clear automated procedure for being programmed and used. The existing methodologies for programming DMF biochips require an advanced level of understanding of software programming and of the architecture of the biochip itself. These skills are not commonly found in potential target users of this technology, such as biologists and chemists. A fully automated compilation pipeline able to translate biochemical protocols expressed in a high-level representation into the low-level biochip control sequences would enable access to the DMF technology by a larger number of researchers and professionals. The advanced scheduling and routing methodologies investigated by this project are one of the main obstacles towards broadly accessible DMF technology. This is particularly relevant for researchers and small businesses which cannot afford the large pipetting robots commonly used to automate biochemical industrial protocol. One or more DMF biochips can be programmed to execute ad-hoc repetitive and tedious laboratory tasks. Thus, freeing qualified working hours for more challenging laboratory tasks. In addition, the scheduling and routing methodologies targeted by this project enable for online decisions, such as controlling the flow of the biochemical protocols depending upon on-the-fly sensing results from the processes occurring on the biochip. This opens for a large set of possibilities in the biochemical research field. For instance, the behavior of complex biochemical protocols can be automatically adapted during execution using decisional constructs (if-then-else) allowing for real-time protocol optimizations and monitoring. From a scientific perspective, this project would enable cross-field collaboration, develop new methodologies, and potentially re-purpose those techniques that are well known in one research field to solve problems of another field. For the proposed project, interesting possibilities include adapting advanced routing and graph-related algorithms or applying well-known online algorithms techniques to manage the real-time flow control nature of the biochemical protocol. The cross-field nature of the project has the potential of providing a better understanding of how advanced scheduling and routing techniques can be applied in the context of a strongly constrained application such as DMF biochips. Thus, laying the ground for novel solutions, collaborations, and further research. Finally, it should be mentioned that the outcome of this project, or of a future larger project based on the proposed explorative research, is characterized by a concrete business value. Currently, some players have entered the market with DMF biochips built to perform a specific biochemical functionality [12,13]. A software stack that includes compilation tools supporting programmability and enabling the same DMF biochip to perform different protocols largely expands the potential market of such technology. This is not the preliminary aim of this research project, but it is indeed a long-term possibility.

News / coverage

Participants

Project Manager

Luca Pezzarossa

Assistant Professor

Technical University of Denmark
DTU Compute

E: lpez@dtu.dk

Eva Rotenberg

Associate Professor

Technical University of Denmark
DTU Compute

Lene M. Favrholdt

Associate Professor

University of Southern Denmark
Department of Mathematics and Computer Science

Kategorier
Explore project

Automated Verification of Sensitivity Properties for Probabilistic Programs

Project type: Explore Project

Automated Verification of Sensitivity Properties for Probabilistic Programs

Sensitivity measures how much program outputs vary when changing inputs. We propose exploring novel methodologies for specifying and verifying sensitivity properties of probabilistic programs such that they (a) are comprehensible to everyday programmers, (b) can be verified using automated theorem provers, and (c) cover properties from the machine learning and security literature.

This work will bring together two junior researchers who recently arrived in Denmark and obtained their PhDs working on probabilistic verification.

Project description

Our overall objective is to explore how automated verification of sensitivity properties of probabilistic programs can support developers in increasing the trust in their software through formal assurances.

Probabilistic programs are programs with the ability to sample from probability distributions. Examples include randomized algorithms, where sampling is exploited to ensure that expensive executions have a low probability, cryptographic protocols, where randomness is essential for encoding secrets, and statistics, where programs are becoming a popular alternative to graphical models for describing complex distributions.

The sensitivity of a program determines how its outputs are affected by changes to its input; programs with low sensitivity are robust against fluctuations in their input – a key property for improving trust in software. Minor input changes should, for example, not affect the result of a classifier learned from training data. In the probabilistic setting, the output of a program depends not only on the input but also on the source of randomness. Hence, the notion of sensitivity – as well as techniques for reasoning about it – needs refinement.

Automated verification takes a deductive approach to proving that a program satisfies its specification: users annotate their programs with logical assertions; a verifier then generates verification conditions (VCs) whose validity implies that the program’s specification holds. Deductive verifiers are more complete and more scalable than fully automatic techniques but require significant user interaction. The main challenge for users of automated verifiers lies in finding suitable intermediate assertions, particularly loop invariants, such that an automated theorem prover can discharge the generated VCs. A significant challenge for developers of automated verifiers is to keep the amount and complexity of necessary annotations as low as possible.

Previous work [1] co-authored by the applicants provides a theoretical framework for reasoning about the sensitivity of probabilistic programs: the above paper presents a calculus to carry out “pen-and-paper” proofs of sensitivity in a principled and syntax-directed manner. The proposed technique deals with sampling instructions by requiring users to identify suitable probabilistic couplings, which act as synchronization points, on top of finding loop invariants. However, the technique is limited in the sense that it does not provide tight sensitivity bounds when changes to the input cause a program to take a different branch on a conditional.

Our project has four main goals. First, we will develop methodologies that do not suffer from the limitations of [1]. We believe that conditional branching can be treated by carefully tracking the possible divergence. Second, we will develop an automated verification tool for proving sensitivity properties of probabilistic programs. The tool will generate VCs based on the calculus from [1], which will be discharged using an SMT solver. In designing the specification language, we aim to achieve a balance so that (a) users can conveniently specify synchronization points for random samples (via so-called probabilistic couplings) and (b) existing solvers can prove the resulting VCs. Third, we aim to aid the verification process by assisting users in finding synchronization points. Invariant synthesis has been extensively studied in the case of deterministic programs. Similarly, coupling synthesis has been recently studied for the verification of probabilistic programs [2]. We believe these techniques can be adapted to the study of sensitivity. Finally, we will validate the overall verification system by applying it to case studies from machine learning, statistics, and randomized algorithms.

 

Participants

Alejandro Aguirre

Postdoc

Aarhus University
Department of Computer Science

Christoph Matheja

Assistant Professor

Technical University of Denmark
DTU Compute

Kategorier
Explore project

Understanding Biases and Diversity of Big Data used for Mobility Analysis

Project type: Explore Project

Understanding Biases and Diversity of Big Data used for Mobility Analysis

Summary
Our capabilities to collect, store and analyze vast amounts of data have greatly increased in the last two decades, and today big data plays a critical role in a large majority of statistical algorithms. Unfortunately, our understanding of biases in data has not kept up. While there has been lot of progress in developing new models to analyze data, there has been much less focus on understanding the fundamental shortcomings of big data. This project will quantify the biases and uncertainties associated with human mobility data collected through digital means, such a smartphone GPS traces, cell phone data, and social media data. Ultimately, we want to ask the question: is it possible to fix big mobility data through a fundamental understanding of how biases manifest themselves?
Value Creation
We expect this project to have a long-lasting scientific and societal impact. The scientific impact of this work will allow us to explicitly model bias in algorithmic systems relying on human mobility data and provide insights into which population are left out. For example, it will allow us to correct for gender, wealth, age, and other types of biases in data globally used for epidemic modeling, urban planning, and many other usecases. Further, having methods to debias data will allow us to understand what negative impacts results derived from biased data might have. Given the universal nature of bias, we expect our developed debiasing frameworks will also pave the way for quantitative studies of bias in other realms of data science. The societal impact will be actionable recommendations provided to policy makers regarding: 1) guidelines for how to safely use mobility datasets in data-driven decision processes, 2) tools (including statistical and interactive visualizations) for quantifying the effects of bias in data, and 3) directions for building fairer and equitable algorithm that rely on mobility data. It is important to address these issues now, because in their “Proposal for a Regulation on a European approach for Artificial Intelligence” from April 2021 the European Commission (European Union) outlines potential future regulations for addressing the opacity, complexity, bias, and unpredictability of algorithmic systems. This document states that high-quality data is essential for algorithmic performance and suggest that any dataset should be subject to appropriate data governance and management practices, including examination in view of possible biases. This implies that in the future businesses and governmental agencies will need to have data-audit methods in place. Our project addresses this gap and provides value by developing methodologies to audit mobility data for different types of biases — producing tools which Danish society and Danish businesses will benefit from.

Participants

Project Manager

Vedran Sekara

Assistant Professor

IT University of Copenhagen
Department of Computer Science

E: vsek@itu.dk

Laura Alessandretti

Associate Professor

Technical University of Denmark
DTU Compute

Manuel Garcia-Herranz

Chief Scientist

UNICEF
New York

Elisa Ormodei

Assistant Professor

Central European University

Kategorier
Explore project

Ergonomic & Practical Effect Systems

Project type: Explore Project

Ergonomic & Practical Effect Systems

Summary

Effect systems are currently a hot research subject in type theory. Yet many effect systems, whilst powerful, are very complicated to use, particularly by programmers that are not experts at type theory. Effect systems with inference can provide useful guarantees to programming languages, while being simple enough to be used in practice by everyday programmers.

Building on the Boolean unification-based polymorphic effect system in the Flix programming language, we want to pursue two practical short-term objectives: to (a) improve the quality of effect error messages, and to (b) develop techniques to improve the performance of Boolean unification and effect inference. Thus laying the foundation for a more ambitious objective: The Futhark programming language supports a form of referentially transparent in-place updates, controlled by a system of uniqueness types inspired by Clean, but which is too limited in the presence of polymorphic higher-order functions. Recasting the type system in terms of effects, based on the one in Flix, might provide a more intuitive system.

A unique aspect of this project is that it brings together two programming language researchers, one from Aarhus and one from Copenhagen, who are both working on full-blown programming language implementations.

Value Creation

We address value creation following the three outlined categories:

Scientific Value: We see two clear publishable scientific contributions: (a) new techniques to improve the performance of Boolean unification and (b) new applications of type and effect systems based on Boolean unification.

Capacity Building: Flix and Futhark are the two the major academic efforts towards building new programming languages in Denmark. Bringing the two research groups together will facilitate knowledge sharing and technology transfer; enabling both projects to thrive and grow even further. This unique opportunity exists because both languages are based on similar technology and because they do not compete in the same space. Success for one is not at the expense of the other, and they can rise together.

Business and Societal Value: A significant amount of research effort has been expended on designing effect systems. Despite widespread belief that such systems can lead to safer programs, few systems have been implemented in real-world programming languages. By focusing on improving the ergonomics, we want to make these technologies more accessible. Being the designers of Flix and Futhark, we are in great position to conduct such work. We can show the way for other mainstream programming languages by having real, full-blown implementations.

After decades of relative stagnation, programming languages are now rapidly absorbing features previously only seen in obscure or academic programming languages. Java and C# and prominent examples of originally very orthodox object-oriented languages that have been augmented with concepts from functional programming. We believe that effect systems and other fancy type system features are a logical next step, but before they can be added to mainstream languages, it must be shown that they can be designed and implemented in a form that will be palatable to industrial users. Thus, while Flix and Futhark may or may not be the languages of the future, we believe that our research can help impact the direction of future programming languages by providing solid formal foundations and real-world implementations that others can build on directly or indirectly.

Nyheder / omtale

Deltagere

Project Manager

Magnus Madsen

Associate Professor

Aarhus University
Department of Computer Science

E: magnusm@cs.au.dk

Troels Henriksen

Assistant Professor

University of Copenhagen
Department of Computer Science

Kategorier
Explore project

Hardware/software Trade-off for the Reduction of Energy Consumption

DIREC-projekt

Hardware/Software Trade-offs for the Reduction of Energy Consumption

Resumé

Computerenheder bruger en betydelig mængde energi. Implementering af algoritmer i hardware ved hjælp af feltprogrammerbare gate-arrays (FPGA’er) kan være mere energieffektive end at udføre dem i software i en processor.

Dette projekt udforsker klassiske sorterings- og stifindende algoritmer og sammenligner deres energieffektivitet og ydeevne, når de implementeres i hardware.

Brugen af FPGA’er er stigende i mainstream computing, og projektet kan gøre det muligt for softwareudviklere at bruge et funktionelt sprog til effektivt at implementere algoritmer i FPG’er og reducere energiforbruget.

Computing devices consume a considerable amount of energy. Within data centers this has an impact on climate change and in small embedded systems, i.e., battery powered devices, energy consumption influences battery life. Implementing an algorithm in hardware (in a chip) is more energy efficient than executing it in software in a processor. Up until recently processor performance and energy efficiency have been good enough to just use software on a standard processor or on a graphic processing unit. However, this performance increase comes to an end and energy-efficient computing systems need domain specific hardware accelerators.

However, the cost of producing a chip is very high. Between fixed hardware and software there is the technology of field-programmable gate arrays (FPGAs). FPGAs are programmable hardware, the algorithm can be changed at runtime. However, FPGAs are less energy efficient than chips. We expect that for some algorithms an FPGA will be more energy efficient than the implementation in software. The research question is whether and how it is possible to reduce energy consumption of IT systems by moving algorithms from software into hardware (FPGAs). We will do this by investigating classic sorting and path-finding algorithms and compare their energy-efficiency and, in addition, their performance.

Such results are essential to both data centers as well as embedded systems. However, the hardware design of these accelerators is often complex, and their development is time-consuming and error-prone. Therefore, we need a tool and methodology that enables software engineers to design efficient hardware implementation of their algorithms. We will explore a modern hardware construction language, Chisel. Chisel is a Scala-embedded hardware construction language that allows to describe hardware in a more software-like high-level language. Chisel is the enabling technology to simplify the translation of a program from software into hardware. This project will furthermore investigate the efficiency of using the functional and object-oriented hardware description language Chisel to express algorithms efficiently for execution in FPGAs.

Programs running on a general-purpose computer consume a considerable amount of energy. Some programs can be translated into hardware and executed on an FPGA. This project will explore the trade-offs between executing a program in hardware and executing it in software relative to energy consumption.

Scientific Value
The FPGA and software implementations of path-finding algorithms have recently been evaluated in the lense of performance, e.g., [?], whereas sorting algorithms have also been evaluated on energy consumption, e.g., [2]. Here FPGAs performed better than CPU in many cases and with similar or reduced energy consumption. The language used for implementation is Verilog and C which is then translated to Verilog using Vivado HLS. In this project, we will implement the algorithms in hardware using Chiesl and evaluate their performance and energy consumption. DTU and RUC will advance the research in the design and testing of digital systems for energy saving. Our proposed approach provides a general software engineering procedure that we plan to validate with standard algorithms used in cloud applications. This research will drive the adaption of hardware design methods to the education curriculum towards modern tools and agile methods.

Capacity Building
The project establish a new collaboration between two Danish Universities and is a first step towards building a more energy-aware profile of the Computer Science laboratory FlexLab, RUC. In return FlexLab make FPGAs available to the research assistants at RUC. Thus, this project will improve visibility of energy-aware design IT systems nationally and international. This project with the cooperation between researchers as DTU and RUC will allow Denmark to take the lead in digital research nd development for reduced energy consumption. The upcoming research positions at RUC will contribute to building RUC’s research capacity, and the project will also recruit new junior researchers directly and in future subsequent projects.

Business Value
The changes in the hardware industry indicates that the use of FPGAs will increase: A few years ago Intel bought Altera -one of the two largest FPGA production companies- to include FPGAs in future versions of their processors. Similar, AMD is aiming to buy Xilinx, the other big FPGA vendor. In addition, one can already rent a server in the cloud from Amazon that includes an FPGA. These changes all points towards that FPGAs are entering mainstream computing. Many mainstream programming languages like C# or Java already include functional features such as lambda expressions or higher-order functions. The more common languages for encoding FPGAs are Verilog, a C inspired language, and VHDL, a Pascal inspired language, Therefore, it may be efficient for mainstream software developers to use a functional language to efficiently implement algorithms in FPGAs and thus both increase performance and reduce the energy consumption.

Societal Value
Currently ICT consumes approximately 10% of the global electricity and this is estimated to increase to 20% in 2030. Thus, reducing energy consumption of ICT is critical. If successful, this project has the potential to reduce the energy consumption via rephrasing the essential software programs in FPGA units.

Værdi

I øjeblikket forbruger IKT omkring 10% af den globale elektricitet, og dette anslås at stige til 20% i 2030. Derfor er det afgørende at reducere energiforbruget af IKT. Hvis det lykkes, har dette projekt potentiale til at reducere energiforbruget ved at omformulere de væsentlige softwareprogrammer i FPGA-enheder.

Participants

Project Manager

Maja Hanne Kirkeby

Assistant Professor

Roskilde University
Department of People and Technology

E: majaht@ruc.dk

Martin Schoerberl

Associate Professor

Technical University of Denmark
DTU Compute

Mads Rosendahl

Associate Professor

Roskilde Universlty
Department of People and Technology

Thomas Krabben

FlexLab Manager

Roskilde University
Department of People and Technology

Kategorier
Explore project

Explainable AI

Project type: Explore Project

Explainable AI

Artificial Intelligence brings the promise of technological means to solve problems that previously were assumed to require human intelligence, and ultimately provide human-centered solutions that are both more effective and of higher quality in a synergy between the human and the AI system than solutions that are provided by humans or by an AI system alone.

However, compared to traditional problem solving based on logical rules and procedures, some artificial intelligence systems, in particular systems based on neural networks (e.g. as in deep learning), do not offer a human-understandable explanation to the answers given. Lack of explanation is not necessarily a problem, e.g. if the correctness of an answer can be easily validated, such as automatic character recognition subsequently validated by a human. However, in some situations, a lack of explanation may pose severe problems, and may even be illegal as it is the case for governmental decisions.

Participants

Project Manager

Thomas Hildebrandt

Professor

University of Copenhagen
Department of Computer Science

E: hilde@di.ku.dk

Irina Shklovski

Professor

University of Copenhagen
Department of Computer Science

Naja Holten Møller

Assistant Professor

University of Copenhagen
Department of Computer Science

Hugo Lopez

Assistant Professor

University of Copenhagen
Department of Computer Science

Boris Düdder

Associate Professor

University of Copenhagen
Department of Computer Science

Tijs Slaats

Associate Professor

University of Copenhagen
Department of Computer Science

Henrik Korsgaard

Assistant Professor

Aarhus University
Department of Computer Science

Susanne Bødker

Professor

Aarhus University
Department of Computer Science

Lars Kai Hansen

Professor

Technical University of Denmark
DTU Compute

Thomas Bolander

Professor

Technical University of Denmark
DTU Compute

Kim Guldstrand Larsen

Professor

Aalborg University
Department of Computer Science

Thomas Dyhre Nielsen

Professor

Aalborg University
Department of Computer Science

Alessandro Tibo

Assistant professor

Aalborg University
Department of Computer Science

Manfred Jaeger

Associate Professor

Aalborg University
Department of Computer Science

Anders Lyhne Christensen

Professor

University of Southern Denmark
The Maersk Mc-Kinney Moller Institute

Sebastian Risi

Professor

IT University of Copenhagen
Digital Design Department

Lars Rune Christensen

Assistant professor

iT University of Copenhagen
Department of Business IT

Arisa Shollo

Associate Professor

Copenhagen Business School
Department of Digitalization

Rasmus Larsen

AI Specialist

The Alexandra Institute

Peter C. Damm

Applied Research Director

KMD

Mathias Niepert

Manager & Chief Research Scientist

NEC Labs Europe
Heidelberg

Tobias Jacobs

Senior Researcher

NEC Labs Europe
Department of Computer Science

Morten Marquard

Founder & CEO

DCR Solutions

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