Carmela Troncoso among Fortune’s 40 Under 40

Carmela Troncoso among Fortune’s 40 Under 40

Each year, Fortune magazine recognizes the top 40 influencers or emerging leaders aged below 40 years. In a deviation, the magazine has taken cognizance of the monumental challenges and changes witnessed this year by highlighting 40 influential people in five categories instead of one: finance, technology, healthcare, government and politics, and media and entertainment. Carmela Troncoso, head of the Security and Privacy Engineering Lab (SPRING) in EPFL’s School of Computer and Communication Sciences (IC), figures in the technology category for her leading role in building the Decentralized Privacy-Preserving Proximity Tracing system, or DP-3T.

DP-3T stores temporary, anonymized contact data on a user’s phone, rather than on a central server, making hacks or misuse much harder. The system’s design helped guide Apple and Google’s development of a shared contact-tracing protocol, which is now being used by COVID-19 tracing apps across Europe and the U.S. DP-3T is the basis for SwissCovid, a tracing app that serves as a useful tool in stemming the spread of the disease in Switzerland.

The work done by Troncoso and her team is quite extraordinary because privacy protection, a major anxiety for tech users, is at the core of DP-3T. Privacy concerns always topped Troncoso’s agenda. In a statement to Schweizer Illustrierte few months ago, she evinced those very thoughts: “The technological and social challenges [around the protection of privacy in IT systems] give me sleepless nights.” The result is DP-3T, a secure and privacy-preserving system that is playing a crucial role in fighting the pandemic.

Her inclusion in Fortune’s 40 Under 40 provides Troncoso a platform to showcase the work done by the SPRING lab in alleviating the negative impact of technology on society, such as privacy concerns, and presenting purely system-based solutions rather than data-driven platforms. She believes that the recognition by Fortune vindicates that approach. In her own words, “The SwissCovid tracing app is also purely a systems solution, it has no data. For the first time we have governments that have gone for data-less solutions and the fact that Fortune has recognized this paradigm change is key for privacy.”



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Novel Frequency Division Technique to Generate Low-Noise Microwave Signals

Researchers at EPFL’s Laboratory of Photonics and Quantum Measurements (K-Lab), Trinity College Dublin (TCD), and Dublin City University (DCU) have teamed up to develop a new technique for generating variable low-noise microwaves with a single optical microresonator. The paper was recently published in Science Advances.

Optical frequency combs (OFCs) based on femtosecond pulse lasers have the potential to revolutionize the fields of optical metrology and spectroscopy. Development of the frequency division technique has allowed the use of photodetection of pulse trains to synthesize microwaves with lowest phase noise levels. However, the use of mode-locked laser-based OFCs has been limited to the laboratory due to their unwieldy size, high power consumption, and delicate structure. Although some approaches have been proposed to make OFCs field-deployable, they have limitations that prevent wider application.

The new research proposes a frequency division scheme in which two compact frequency combs, (a soliton microcomb and a semiconductor gain-switched comb) are combined to demonstrate low-noise microwave generation. Using the technique, the team successfully generated new microwaves that showed much lower phase-noise levels than those of a microresonator frequency comb oscillator and off-the-shelf microwave oscillators.

The technique presented by the authors enables spectral purity transfer between different microwave signals. Lead author Wenle Weng explains:

“Traditionally, executing perfect microwave frequency division in a variable fashion has not been easy. Thanks to the fast-modulated semiconductor laser developed by our colleagues at TCD and DCU, now we can achieve this using a low-cost photodetector and a moderate control system.”

While the traditional optical injection locking method uses a continuous-wave (CW) laser as the master, the new scheme locks a semiconductor laser to the entire microcomb, transferring both the carrier phase coherence and the soliton repetition rate spectral purity to the gain-switched laser (GSL). Consequently, the GSL can generate additional comb teeth that are fully coherent and equally spaced, facilitating the application of high-repetition rate microcombs in metrology and spectroscopy.

With the ability to be portable and mass-produced, the variable microwave oscillator and frequency comb generator developed by the team can revolutionize the market for portable low-noise microwave and frequency comb sources.

The research was funded by Swiss National Science Foundation; Defense Advanced Research Projects Agency, Defense Sciences Office (US); Science Foundation Ireland (SFI); and SFI/European Regional Development Fund.


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3D-ICE Thermal Modeling Research Wins Retrospective Most Influential Paper Award

Given the fast pace of research, very few scientific studies stand the test of time. Even rarer is a study that continues to influence research a decade after its first publication. That distinction goes to “3D-ICE: Fast Compact Transient Thermal Modeling for 3D ICs with Inter-Tier Liquid Cooling,” a paper presented at the IEEE/ACM International Conference on Computer-Aided Design (ICCAD) 2010 Conference. It has been selected as the winner of the prestigious ICCAD 2020 – Ten Year Retrospective Most Influential Paper Award , which is one the most prestigious given in the Electronic Design Automation (EDA) community about industrial and academic relevance of a technical paper.

The ICCAD Executive Committee has recognized this work about the design of open-source 3D Interlayer Cooling Emulator (3D-ICE) tool (link: https://www.epfl.ch/labs/esl/research/open-source-software-projects/3d-ice/) for compact transient thermal modeling of 2D/3D multi-processor system-on-chip (MPSoC) with liquid-cooling as “the most influential on research and industrial practice in computer-aided design of integrated circuits over the ten years since its original appearance at ICCAD.” The authors of the paper are Arvind Sridhar, Alessandro Vincenzi, Martino Ruggiero, David Atienza (all from the Embedded Systems Laboratory – ESL at EPFL), and Thomas Brunschwiler (IBM Zurich Research Laboratory).

As the researchers argue in their paper, the vertical integration of high-performance integrated circuits in the form of 3D stacks (3D ICs) is highly demanding since the effective areal heat dissipation increases with number of dies generating high chip temperatures. To deal with the thermal challenge, inter-tier integrated microchannel cooling is a promising and scalable solution. However, a robust design of a 3D IC and its subsequent thermal management requires accurate modeling of the effects of liquid cooling with respect to other cooling solutions regarding the thermal behavior of the IC. Therefore, the authors developed 3D-ICE as a compact transient thermal model (CTTM) for the thermal simulation that can consider the non-linear thermal properties of liquids and nano-scale materials used in 2D and 3D Multi-Processor System-on-Chip (MPSoC) architectures with multiple inter-tier microchannel liquid cooling. The model offers significant speed-up over a typical commercial computational fluid dynamics simulation tool while preserving accuracy. Based on 3D-ICE, the study presented a thermal simulator capable of running in parallel on multicore architectures and possible to be parallelized in GPUs, offering further savings in simulation time and higher efficiency.

3D-ICE is a Linux-based Thermal Emulator Library written in C, which can perform transient thermal analyses of vertically stacked 3D integrated circuits with inter-tier Microchannel Liquid Cooling. This approach and tool developed at EPFL was used in the design of Aquasar (the first chip-level water-cooled server by IBM). A decade after it was developed, 3D-ICE continues to be the go-to tool for more than 1500 teams worldwide.

The ICCAD award recognizes the worldwide relevance of EPFL’s work on micro-electronics and MPSoC thermal-aware design. It will be presented on November 2 at the opening session of the Virtual Event of ICCAD 2020.

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Black-box Estimation of the Bayes Risk Using ML Methods

One of the key researches in the domain of quantitative information flow (QIF) is to effectively estimate information leaks in a system in order to prevent adversarial attacks. Most existing approaches are based on the white-box approach. However, this approach is often impractical due to the size or complexity of its internals, or the presence of unknown factors. This and other challenges forced a shift in focus to investigate methods for measuring a system’s leakage in a black-box manner.

Thus far, the only approach for the black-box estimation has been founded on the frequentist paradigm, which cannot be scaled to real-world problems and be applied to systems with continuous outputs (e.g., time side channels, network traffic). To address that problem, EPFL’s Giovanni Cherubin and coauthors have proposed to leverage an analogy between Machine Learning (ML) and black-box leakage estimation to show that the Bayes risk of a system can be estimated by using a class of ML methods.

In their paper, which was presented at the IEEE Symposium on Security and Privacy (2019), the researchers took cognizance of a fundamental equivalence between ML and black-box leakage estimation to demonstrate that any ML rule from a certain class (the universally consistent rules) can be used to estimate with arbitrary precision the leakage of a system. More specifically, their work is based on the nearest neighbor principle, which significantly reduces the number of black-box queries required for a precise estimation and exploits a metric on the output space to achieve a considerably faster convergence than frequentist approaches. The research adds a completely new class of estimators that can be used in practical applications.

Based on their findings, the researchers have developed a tool called F-BLEAU (Fast Black-box Leakage Estimation AUtomated). The tool computes nearest neighbor and frequentist estimates, and selects the one converging faster. F-BLEAU considers a generic system as a black-box, taking secret inputs and returning outputs accordingly, and it measures how much the outputs “leak” about the inputs.

F-BLEAU is available as an open source software at https://github.com/gchers/fbleau

Giovanni Cherubin is Postdoctoral Fellow in Machine Learning and Security at EPFL. He is the recipient of an EcoCloud post-doctoral fellowship since October 2018, and his work on F-BLEAU was supported by that fellowship.

G. Cherubin, K. Chatzikokolakis and C. Palamidessi, “F-BLEAU: Fast Black-Box Leakage Estimation,” 2019 IEEE Symposium on Security and Privacy (SP), San Francisco, CA, USA, 2019, pp. 835-852, doi: 10.1109/SP.2019.00073.

Full text pdf at https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8835250

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Microchannel Network Inspired by the Human Circularity System

While scientists have successfully reduced the size and costs of electronic components, a major challenge faced by such tiny devices is the absence of an optimum thermal and energy management technology. To bridge that gap, Elison Matioli and his colleagues at EPFL’s Power and Wide-band-gap Electronics Research Laboratory (POWERlab) have developed a novel microchannel network that not only cools electronic components but also makes them energy efficient.

Since electronic components are averse to high temperatures, they are usually cooled down by means of conventional fan-cooled heat exchangers or more complex fluid-carrying microchannels running through them. The microchannels need to be extremely narrow and small to have the required impact, but that necessitates a higher amount of pressure for proper flow of the fluid. That translates into higher energy consumption. To address that energy challenge, Matioli and others have integrated microfluidics and electronics within the same semiconductor substrate. This embedded approach is unlike state-of-the-art technology, where electronics and cooling are treated separately.

The EPFL researchers used a chip containing a thin layer of a semiconductor called gallium nitride (GaN) on top of a thicker silicon substrate. In a departure from existing techniques, they carved the microchannels within the substrate and aligned them with the parts of the chip that tend to heat up the most, thus helping the system cool down efficiently. For reducing the energy needed to pump the fluid through the microchannels, the researchers drew inspiration from the human circulatory system, which comprises larger blood vessels that become thinner and transform into capillaries in certain areas of the body. They designed the microchannel network with wider channels that taper in the exact location where the heat builds up more. This radically reduced the total amount of energy needed to push the fluid. Experiment results showed an unprecedented coefficient of performance (exceeding 10,000) for single-phase water-cooling of heat fluxes exceeding 1 kilowatt per square centimetre, corresponding to a 50-fold increase compared to straight microchannels.

The research paper “Co-designing electronics with microfluidics for more sustainable cooling” is published in the latest issue of Nature.

van Erp, R., Soleimanzadeh, R., Nela, L. et al. Co-designing electronics with microfluidics for more sustainable cooling. Nature 585, 211–216 (2020). https://doi.org/10.1038/s41586-020-2666-1



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David Atienza, Babak Falsafi, Marting Jaggi, and Mathias Payer

Facebook-EPFL Joint ML Research Engagement

Facebook and EPFL have initiated a collaborative program that aims to carry out seminal research with common meeting points for both organizations. Facebook seeks to leverage EPFL’s proven expertise in Computer Science and Engineering to enable the flow of technology from one of the most renowned research institutions to the leading American social media conglomerate. The collaboration will also help the latter strengthen its position in Switzerland and gain access to some of the best academic minds in Europe.

The following projects have already been lined up for the collaborative Full-System Accelerated and Secure ML Collaborative Research program:

  • Training for Recommendation Models on Heterogeneous Servers
  • Distributed Transformer Benchmarks
  • Full-System API Inference to Enforce Security
  • Communication Stacks for µServices in Datacenters

Each of these projects will be conducted by a member of the expert team from EPFL. The team includes David Atienza, Babak Falsafi, Martin Jaggi, and Mathias Payer. Babak Falsafi will be the point of contact for the engagement.

Training for Recommendation Models on Heterogeneous Servers   

This project aims to develop strategies to automatically select the best accelerator to run a specific DNN training. The research by David Atienza and team will develop the necessary software libraries to allocate workload efficiently by considering performance, power, and accuracy constraints. Meta-learning algorithms will be created to train DL models and configure their hyper-parameters in an automated way, outperforming current state-of-the- art approaches. This approach is expected to result in significant savings in the total training time and improved robustness against minimization for smaller memory size designs.

Distributed Transformer Benchmarks

MLBench, a framework for distributed machine learning, aims to perform the role of an easy-to-use and fair benchmarking suite for algorithms as well as for systems (software frameworks and hardware). It will provide re-usable and reliable reference implementations of distributed ML training algorithms. MLBench renders support to a wide range of platforms, ML frameworks, and machine learning tasks. Its goal is to benchmark all/most currently relevant distributed execution frameworks. Lead researcher Martin Jaggi and team will soon release the first results and reference code for distributed training (starting with Cifar10 and ImageNet, in both PyTorch and TensorFlow).

Full-System API Inference to Enforce Security

Mathias Payer and team aim to build an API flow graph (AFG) that encodes all valid API interactions and their parameters. The proposed algorithm will build the global AFG by analyzing all uses of a function on the system’s source code. The researchers will leverage test projects that provide a large corpus of test cases and input files for a wide variety of programs. The data set will help infer API usage by monitoring the state construction through the provided seeds and examples.

Communication Stacks for µServices in Datacenters

In this study, Babak Falsafi and others will investigate technologies to support communication in microservices. The research is an extension of their prior work on tighter integration of network with memory with support for memory pooling and RPC scheduling. It aims to tackle the software bottleneck in communication for microservices and address challenges such as memory scalability for RPC, software stacks for high fan-out RPC processing, higher-level object access semantics via RPC to avoid multiple roundtrips, and support for data transformation across diverse language and software ecosystem boundaries. The researchers will investigate codesigned RPC technologies with hardware terminating protocols that enable serving packets directly out of CPU’s SRAM to eliminate DRAM capacity and bandwidth provisioning and enable a new class of RPC substrate that is inherently technology-scalable. They propose to investigate optimizations for data transformation for common case data formats running conventional CPU’s. They will delve into the integration of data transformation into an optimized RPC stack (from above) to identify opportunities for data placement, reduction in data movement and buffering on commodity hardware. Technologies for hardware/software co-design of data transformers will also be within the scope of the work.

The Facebook-EPFL collaborative engagement has been approved for funding for an initial period of one year, with an expected renewal each year for at least three years. Each project includes a grant of CHF 200,000 per year, which will be used to financially support one student.

For more details of the individual projects, visit:


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