New Transistor Design Reduces Energy Dissipation in High-power Applications

Energy dissipation is one of the biggest challenges in running high-power applications, such as an electric vehicle or a solar panel installation. Although state-of-art power converters are built to tolerate high voltages while converting energy to make our electronic devices work, they lose almost a fifth of their energy in the process. Researchers at the Power and Wide-band-gap Electronics Research Laboratory (POWERLAB) in EPFL’s Institute of Electrical Engineering (IEL) have been working to circumvent this problem. In a collaboration with other scholars in the field, the POWERLAB team led by Elison Matioli has just published the results of their research in Nature Electronics.

In their paper, the researchers introduce a nanowire-based device to create high-electron-mobility tri-gate transistors for power-conversion applications. Based on nanoscale structures, the novel transistor design significantly reduces heat loss during the energy conversion process. The nano-transistors work well in high-power applications by tackling electrical resistance, the main culprit in electric power loss. As Elison Matioli explains, “We see examples of electric power losses every day, such as when the charger of your laptop heats up.” The problem multiplies in high-power applications because the resistance increases with an increase in the voltage of the semiconductor components. That has a direct impact on the efficiency of the application. For example, it could reduce the range of an EV because power losses occur in the vehicle when charging.

Compared to traditional transistors, the new nano-transistor has less than 50% resistance even at high voltages of more than 1000 volts. It delivers such high efficiency because of two innovative design elements. First, it incorporates multiple two-dimensional electron gas channels, which allow more electrons to flow and improve conductivity. Second, it uses nanowires made of gallium nitride, a semiconducting material best suited for power applications. Further, the nanowires have higher resistance due to their funnel-like arrangement.

The new technology is still being tested, but the researchers are confident that they can quickly transition to large-scale production. That is very good news for a market ripe for transistors that can perform efficiently at high voltages. The POWERLAB team has already received several inquiries from manufacturers to work together to take the technology forward.

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Can Influencers Sway Public Opinion?

This is the age of social media, where the opinions of celebrities make a big difference. Influential people are roped in to endorse a consumer product, a social movement, an election campaign, or even a vaccination drive. The assumption is that the influencers have enough clout to sway opinions and thoughts. However, as two separate research papers from EPFL have discovered, that is not always the ground reality. Instead, the experiments suggest that opinions are too well entrenched to change despite the best efforts from celebrity spokespersons. Surprisingly, the likeability of the concerned celebrity has little bearing on the outcome.

In the first study, EPFL’s Andreas Spitz and Robert West teamed up with Ahmad Abu-Akel (University of Lausanne) to conduct a survey-based randomized controlled trial, in which respondents’ opinions were recorded on an array of current topics, as well as their attitude towards a set of celebrity spokespersons. The respondents were then presented with the opinions of spokespersons who were categorized as:

  1. a liked celebrity with a disagreeing opinion;
  2. a disliked celebrity with a disagreeing opinion;
  3. an expert with a disagreeing opinion;
  4. and a disliked but agreeing with celebrity.

Contrary to expectations, the experiment revealed that the respondents held on to their views firmly, regardless of the celebrity inputs or their esteem in the eyes of the respondents. It was also clear that respondents liked to hear an opinion identical to their own even if it came from a disliked celebrity. Conversely, a dissenting opinion by a celebrity or expert reduced the respondent’s empathy for that person.

While the first study considered the rather more polarized opinions on a range of topics, the second randomized control study by the same researchers centered on COVID-19, a common challenge facing humanity where most opinions might be expected to veer in the same direction. To test the response to social distancing, calls were issued on Facebook to practice that safety norm. The calls were randomly attributed to an expert (Dr. Anthony Fauci), a celebrity, and a government official. The impact of that attribution was recorded in terms of the willingness of the participants to share the message with their contacts. The results showed that the messages attributed to the expert were reshared most often, and were thus most effective. The messages attributed to the official came in next, while those by the celebrity were least effective. The results of this study could be used for future campaigns related to a health crisis.

Both studies demonstrate that it is very easy for well-intended messages to have a completely undesirable impact on public opinion. On the other hand, proper messaging on a crisis can have a positive impact and help build societal consensus.

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EPFL’s PowerSGD Algorithm Features in Software Industry

Artificial neural networks, a subset of machine learning, are having a major impact on many facets of life and industry. Rapid advancements in the field have allowed us to intervene in, and improve, processes as varied as voice and image recognition, development of new drugs, and reducing fraudulent activities. They have even helped those with hearing impairments to discern and isolate required sounds amidst a cacophony. But as the ambit of neural networks increases, so does their size. A huge bandwidth running into several PetaBytes is required to achieve an accurate model. To address that problem, two EPFL students have developed PowerSGD, an algorithm that allows compression of the needed bandwidth without compromising the accuracy of the training.

Thijs Vogels and Sai Praneeth Karimireddy are pursuing their Ph.D. under the guidance of Professor Martin Jaggi, head of the Machine Learning and Optimization Laboratory at the School of Computer and Communications Sciences (IC). In their research, Vogels and Karimireddy have applied the power method to achieve a reduction of up to 99% of the communication among Graphics Processing Units without affecting the model’s accuracy. Apart from the communication compression, PowerSGD also helps crunch energy consumption, thus playing a role in the fight against climate change.

The algorithm has already been adopted by the software industry, including the world’s most popular deep learning software PyTorch. PyTorch is an extremely versatile software employed by 80% of academic publications using deep learning. It is also used by Tesla’s Autopilot AI application and Facebook’s translation software. The newest version (PyTorch 1.8) comes with PowerSGD built-in, allowing users to activate communication compression with a simple software switch. Apart from PyTorch, PowerSGD also features in Open-AI’s DALL-E, which can generate creative images from text.

Carrying their work forward, the EPFL researchers are applying the same principle to decentralized learning. That is a major step forward because it could help mitigate the risk of data leakage and privacy concerns about sensitive information such as medical records or data stored on mobile phones.

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New Protocol to Help Prevent Transmission of SARS-CoV-2 Strains

Just as the world began to heave a sigh of relief that the worst of the novel coronavirus was over, the virus has mutated into new strains that are even more contagious than the first.  Two of the safety precautions advocated to prevent transmission of the virus—wearing masks and maintaining social distancing—are no longer considered sufficient to guarantee protection from the new virus strains. In that precarious situation, it becomes imperative to find new means of fighting the spread of the disease. Enter CrowdNotifier, a new protocol developed in part at EPFL. It alerts people who attended an event where there was a risk of COVID-19 infection.

In many cases, it is unavoidable to hold meetings in enclosed spaces. Despite the protocols of wearing masks and safe distancing, such events carry the risk of transmission. With CrowdNotifier, it is possible to alert participants in case any of the attendees was a carrier of the virus and later tests positive for COVID-19. The alert is sent to their smartphones through an app. In Switzerland, the protocol works with the NotifyMe Check-in app developed by Ubique, the same company that developed SwissCovid. The app is available for download on both Apple’s App Store and Google’s Play Store.

NotifyMe has several distinctive features compared to other tracing apps. It works in a decentralized manner on each user’s smartphone, which protects privacy. Neither does it create a centralized database, nor does it store any private information. When a user enters a venue, it is necessary to scan a QR code with the app. That allows the phone to keep an encrypted record of the event sans any private data such as the phone number. If any of the participants at the event turns out to be infectious, the contact tracing features kick in by communicating the details of the event to the organizer. When the organizer uploads a decryption key to a secure server, it is accessed by the app to alert the user to follow isolation and testing processes.

In its test phase, NotifyMe has been deployed at several meeting rooms, classrooms, and cafeterias, and will be subsequently rolled out across the EPFL campus. There is a likelihood of integrating the app with SwissCovid soon.

Matthias Gäumann, Vice President for Operations and president of the operational Covid commission at EPFL, gave further details about the potential impact of NotifyMe:

“NotifyMe will enable us to beef up our COVID-19 protection plan. It will let the students and staff engage in certain activities on-site if they have to while limiting the risk of creating a transmission chain.”


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New Research Presents First Online FL System

Big tech companies make no secret of the fact that they use artificial intelligence to track your browsing behavior and offer products and services that seem to target your interests and needs. While that helps them offer personalized services, there is the risk of sharing your data with a third party. That raises the specter of compromised digital data. In contrast, Federated Learning (FL) systems are gaining credence because they compute all updates on mobile devices, thus keeping the data local. The drawback of standard FL systems, however, is their unsuitability for applications that require frequent updates online, such as news recommendations.

In a major step forward for FL systems, researchers from EPFL and INRIA have developed the first online FL system called FLeet, which makes it possible to carry out machine learning on mobile devices in real-time without any impact on learning tasks. More importantly, data is not shared with any tech company but remains local and secure. The research recently won the best conference paper at the 2020 ACM/IFIP Middleware Conference.

FLeet manages to deliver the best of both worlds—privacy offered by standard FL systems and the precision of online learning. Helping it do so are two main components: I-Prof, a new lightweight profiler that predicts and controls the impact of learning tasks on mobile devices; and AdaSGD, a new adaptive learning algorithm that is resilient to delayed updates.

Test results have shown that FLeet can deliver a 2.3x quality boost compared to Standard FL, while only consuming 0.036% of the battery per day. I-Prof improves the prediction accuracy up to 3.6× (computation time) and up to 19× (energy), while AdaSGD outperforms alternative FL approaches by 18.4% in terms of convergence speed on heterogeneous data.

Rachid Guerraoui and Anne-Marie Kermarrec, professors at EPFL’s School of Computer and Communication Science and authors in the study, emphasize that today’s smartphones have the power to enable distributed machine learning without having to share raw data with, or rely on, large centralized systems.

As Professor Guerraoui explains, “With FLeet it is possible, while you are using your mobile phone, to use some of its spare power towards machine learning tasks without having to worry that your call or internet search will be interrupted…. we don’t want the machine learning to only be happening when you are asleep and your phone is on charge…sometimes we want, and need, real-time information.” Professor Kermarrec elucidates how the findings of their study can foster “truly collaborative learning where local models are aggregated and contribute to the global model but you don’t share raw data and that protects your privacy….”

The researchers are currently exploring options to develop the FLeet prototype into a safe, secure, and usable end product.

“FLeet: Online Federated Learning via Staleness Awareness and Performance Prediction”, authored by Georgios Damaskinos (FaceBook) Rachid Guerraoui, Anne-Marie Kermarrec (EPFL), Rhicheek Patra (Oracle Labs), Francois Taiani (INRIA/University of Rennes), and Vlad Nitu (CNRS)

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Parental Control or Parent Trap?

Over the last decade, the tsunami of mobile services has practically inundated humans and their social behavior, and children are no exception. Even eight-year-olds are glued to their tablets or smartphones. Consequently, they are open to the risks of the all-pervasive Internet world, from violent content to explicit material not suitable for their tender age. It is therefore not surprising that millions of parents install apps that help them monitor and sieve content accessed by their children, and even keep a tab on their location. However, as a recent study* has shown, they could be paying a heavy price for it by jeopardizing private data. While giving parents perceived control over their child’s browsing habits, those apps could be leading them into a ‘parent trap.’

Privacy concerns arising from parental control apps have so far been overlooked by organizations recommending the use of such apps, including some European and other national security centers. Dozens of such popular apps with combined downloads exceeding 20 million in the Google Play Store exist, thereby amplifying the privacy concerns manifold. Recognizing this lacuna in the extant literature, EPFL’s Carmela Troncoso and a group of researchers from IMDEA conducted the first in-depth study of the Android parental control app’s ecosystem from privacy and regulatory point of view.

After a thorough static and dynamic analysis of 46 apps from 43 developers, the researchers concluded that the apps demand more permissions than the top 150 apps in the Google Play Store; and the alarming or hazardous nature of the permissions increases with every new release. The study found that 34% of the apps collect and share personal information without user consent and 72% of the apps share data with third parties.

In many ways, the threat to privacy from some parental control apps is similar to that from spyware. Troncoso, head of the Security and Privacy Engineering Lab (SPRING) at EPFL’s School of Computer and Communication Sciences (IC), expressed her surprise at the extensive presence of surveillant libraries in parental control apps and said, “With some of the apps you can’t look at anything on your phone without information being sent to the backend server.”

The study, which has won the “Prize for the research and Personal Data Protection Emilio Aced” given by the Spanish data protection agency (AEPD), raises a potent question: Does the use of parental control apps justify the dangers arising from the collection and processing of private data? The researchers hope that the study serves as a wake-up call for both parents as well as regulators on the risks associated with these apps. They also call for a security and privacy analysis to help parents download and use an app that protects their data privacy to the maximum extent possible.

* Feal, Álvaro & Calciati, Paolo & Vallina-Rodriguez, Narseo & Troncoso, Carmela & Gorla, Alessandra. (2020). Angel or Devil? A Privacy Study of Mobile Parental Control Apps. Proceedings on Privacy Enhancing Technologies. 2020. 314-335. 10.2478/popets-2020-0029.

Pdf accessible here.

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Photonic Chips: Breakthrough in Data Processing

High-speed mobile networks, connected devices, and widescale adoption of AI technologies contribute to an exponential generation of data that needs to be processed rapidly and efficiently. With conventional chips often found wanting, an international group of scientists has collaborated to present a photonic hardware accelerator that is capable of operating at speeds of “trillions of multiply-accumulate operations per second,” far beyond the capabilities of existing computer processors. The breakthrough study, published in Nature, was carried out by scientists from EPFL, the Universities of Oxford, Münster, Exeter, Pittsburgh, and IBM Research – Zurich.

The scientists have adopted a new approach and architecture that integrates processing and data storage onto a single chip by using light-based or “photonic” processors that outperform conventional electronic chips by achieving “parallelized photonic in-memory computing using phase-change-material memory arrays and photonic chip-based optical frequency combs.” Professor Tobias Kippenberg, EcoCloud member and one of the authors of the study, elaborates on the use of a chip-based “frequency comb” (a technology developed at EPFL) as a light source: “Our study is the first to apply frequency combs in the field of artificial neural networks. The frequency comb provides a variety of optical wavelengths that are processed independently of one another in the same photonic chip.”

Speaking about the advantage of using light-based processors, co-author Wolfram Pernice (Münster University) said that it is “much faster than conventional chips which rely on electronic data transfer, such as graphic cards or specialized hardware like TPU’s.”

The photonic chip developed by the researchers was tested on a neural network that recognizes hand-written numbers. According to the authors, the results of their study indicate the applicability of integrated photonics for parallel, fast, and efficient computational hardware in data-heavy AI applications such as autonomous driving, live video processing, and next-generation cloud computing services.

The study was funded by EPSRC, Deutsche Forschungsgemeinschaft (DFG), European Research Council, European Union’s Horizon 2020 Research and Innovation Programme (Fun-COMP), and Studienstiftung des deutschen Volkes.


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EPFL Scientists Point to Perils of Unregulated AI

Through the years, artificial intelligence has progressed from being a futuristic application in secretive scientific projects to a technology that shapes almost all aspects of modern life. When it first arrived, there were intense discussions on the possibility of AI running amuck, relegating humans to becoming the slaves of AI-run machines. More than half a century of formal research since then, punctuated by major advancements, has established the benefits of AI and made it a defining technology for the future. However, as the applications of AI increase rapidly, the scientific community, as well as lawmakers, are concerned that unregulated AI can lead to misuse and abuse.

In this context, the European Commission took a clear stance by releasing a white paper in February 2020, which could lead to a regulatory framework for AI. That was followed up later in the year by the European Parliament, which adopted a set of proposals on how the EU can best regulate AI to boost innovation, ethical standards, and trust in technology. More recently, scientists convened at a virtual platform called the ‘Governance Of and By Digital Technology’ to deliberate on the principles required to regulate current and future digital technologies and prevent the harmful impact of decision-making algorithms. The event was hosted by EPFL’s International Risk Governance Center (IRGC) and the European Union’s Horizon 2020 TRIGGER Project.

Among the speakers at the conference were EcoCloud members Bryan Ford and James Larus. Professor Ford is Associate Professor at EPFL and head of the Decentralized and Distributed Systems Laboratory (DEDIS) in the School of Communication and Computer Sciences, while Professor Larus is Dean of the IC School and IRGC Academic Director.

Professor Ford called for a “cautious use” of powerful AI technologies but warned against their implementation in defining, implementing, or enforcing the public policy. Instead, policymaking should remain an exclusively human domain. Citing a real-world example, he said, “AI may have many justifiable uses in electric sensors to detect the presence of a car—how fast it is going or whether it stopped at an intersection—but I would claim AI does not belong anywhere near the policy decision of whether a car’s driver warrants suspicion and should be stopped by Highway Patrol.”

Professor Larus emphasized that AI applications have benefits as well as risks, and they must never be allowed to cross the “thin red line” between the two. Regulations are needed to maintain that balance between benefits and risks.

Other speakers at the conference included Stuart Russell, Professor of Computer Science at the University of California, Berkeley. He drew attention to the already evident risks from poorly designed AI systems, which include online misinformation, impersonation, and deception. Marie-Valentine Florin, Executive Director of the IRGC, reminded participants that artificial intelligence is not an end in itself but only a means to an end.

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Rafael Pinot Receives EcoCloud Postdoctoral Research Award

Distributed computing is already playing a pivotal role in a wide range of applications including web-based communications and intelligent information systems. In keeping with this development, EcoCloud presented a research project award to Rafael Pinot for his ongoing work on controlling the spread of fake news or infectious diseases based on the theory of gossip protocols. Rafael just obtained a Ph.D. in Computer Science from Université Paris-Dauphine PSL and CEA LIST institute.

Rafael is working on the project with EPFL professors Rachid Guerraoui, Anne-Marie Kermarrec, and Carmela Troncoso. The research aims to control the dissemination of information in a peer-to-peer network, with a special focus on gossip protocols. Inspired by the literature on adversarial vulnerabilities of these processes, the research will “turn adversaries into algorithms”, essentially using system weaknesses to its advantage. It has two main objectives. First, it aims to design algorithmic solutions to stop or slow the spread of fake news. Second, it will develop efficient methods to detect the source of fake news. While carrying out the study, the researchers will initially investigate the setting where agents communicate using a gossip protocol without learning from each other (dissemination of information) and then adapt their findings to the setting where agents aim to collaboratively learn a machine learning model (peer-to-peer machine learning).

Rafael has a strong academic foundation in Applied Mathematics, having received a BSc and an MSc in the subject from Université de Sorbonne. His work mainly focuses on the security and privacy of machine learning applications. With a penchant for investigating complex data structures, he is particularly interested in the way randomization makes algorithms safer.

Rafael started his academic career in 2017 as a research intern at CEA LIST Institute, where he worked on graph theory and privacy in machine learning. In mid-2018, he received a JSPS fellowship to travel to Japan as visiting researcher at Kyoto University. He continued to pursue his interests in machine learning at Dauphine-PSL University where he also worked on game theory, adversarial machine learning, and trustworthy machine learning.

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New Humanitarian Initiative Brings Digital Technology and Engineering Skills to the Table

Managing humanitarian aid is one of the most important problems in the modern world. It is also a very powerful and direct approach to make a lasting global impact. Keeping that in context, experts at ETH Zurich and EPFL–two of Switzerland’s leading federal institutes of technology–have joined hands with the International Committee of the Red Cross (ICRC) to launch the Engineering Humanitarian Aid initiative.

The partnership brings scientific and technological expertise to tackle three crucial dimensions of humanitarian aid: energy and the environment; data sciences and digital technologies; and personalized health and related technologies. Having worked together on numerous occasions in the past, the three partners will promote developmental cooperation in the field by harnessing digital technology and engineering skills.

Some projects are already underway. Among them is a project led by Carmela Troncoso, professor at EPFL’s Security and Privacy Engineering Laboratory and member of the EcoCloud faculty. The project addresses privacy issues related to biometric data, which seeks to identify the ICRC’s on-the-ground security and privacy requirements when processing beneficiaries’ biometric data, and to design a system that keeps these data confidential.

Another ongoing project uses artificial intelligence to map vulnerable populations. It seeks to compile information from satellite images and social media posts to estimate the size and density of these populations, as well as associated information such as settlement type and changes in population. The work is being conducted by Konrad Schindler and Devis Tuia (EPFL).

In the area of technology for personalized healthcare, a project led by ETH Zurich’s Stephan Wagner aims to augment the availability of medical equipment in conflict zones and reduce wastage.

The collaborators are likely to expand the program over the next year to initiate several joint projects to develop and distribute innovative products and processes. At EPFL, efforts under the initiative are being coordinated by the EssentialTech Center, while at ETH Zurich they are being managed by ETH for Development (ETH4D). The ETH Board has allocated CHF 5 million as seed funding for the projects over a two-year period (2021–22).

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