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: 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

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

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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).


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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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Datashare Network: A Decentralized Search Engine for Journalists

EPFL researchers at the Security and Privacy Engineering (SPRING) Lab, School of Computer and Communication Sciences (IC), have developed a ‘Datashare Network’ that allows investigative journalists to exchange information securely and anonymously. A detailed paper on the subject will be presented by the scientists at the 29th Usenix Security Symposium (USENIX Security ’20), which will be held online from August 12 to 14. The event, which brings together specialists in the security and privacy of computer systems and networks, will undoubtedly draw worldwide attention to the EPFL research.

Important revelations with global implications require the active cooperation and sharing of data among investigative journalists across national borders. This is particularly true for cases involving fraud, deception, and tax evasion. A good example is the infamous Panama Papers case, which brought to light the existence of thousands of shell companies run by many noted politicians, businesspeople, and sports personalities to evade taxes. Such investigations imply the sharing of millions of sensitive documents among international journalists in a secure environment that precludes leaks of any kind. To address that challenge, the International Consortium of Investigative Journalists (ICIJ), comprising 200 members in 70 countries, sought the help of the SPRING Lab. The outcome is the Datashare Network, a fully anonymous, decentralized system for searching and exchanging information.

To ensure anonymity of shared information, the Datashare Network issues virtual secure tokens that journalists can attach to their messages and documents to prove to others that they are ICIJ members. All documents are typically stored on members’ servers or computers, and only essential information critical for further investigation is shared with other users. Using the search engine, users can look for relevant information and then contact, in complete anonymity on either side, the member(s) in possession of that information.

Since users work in different time zones, the network provisions asynchronous searches and responses. In their paper, the research group describes two new secure building blocks developed by them: an asynchronous search engine and a messaging system. The research also introduces the “multi-set private set intersection” (MS-PSI) protocol, which ensures the security of the search engine and mitigates the risk of leaks.

As observed by Carmela Troncoso, head of the SPRING Lab:

“This system, which addresses real-world needs, has enabled SPRING to tackle some interesting challenges…. The hurdles we encountered during the development process…have paved the way to a new area of research with significant potential for other fields.”


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EPFL Authors Win ISVLSI 2020 Best Paper Award

The three-day IEEE Annual Symposium on VLSI (ISVLSI 2020) concluded on July 8 at Limassol, Cyprus. This year, the event attracted more than 190 papers, out of which only 34 were finally accepted after a stringent selection process. The contribution by a group of EPFL scientists not only made it to that august list but also won the Best Paper award at the prestigious event.

The paper “Enabling Optimal Power Generation of Flow Cell Arrays in 3D MPSoCs with On-Chip Switched Capacitor Converters” is a collaborative research by Halima Najibi,1 Alexandre Levisse,2 Marina Zapater,3 and David Atienza,4 who are associated with EPFL’s Embedded Systems Laboratory (ESL). Working with their co-authors Jorge Hunter and Miroslav Vasic, The EPFL group designed an on-chip direct current to direct current (DC-DC) converter to improve FCA power generation in high-performance 3D MPSoCs. They used switched capacitor (SC) technology and explored different design space parameters to achieve minimal area requirement and maximal power extraction.

The proposed converter enables a stable and optimal voltage between FCA electrodes, allowing users to dynamically control FCA connectivity to 3D PDNs and switching off power extraction during chip inactivity. The study demonstrates that regulated FCAs generate up to 123% higher power with respect to the case they are directly connected to 3D PDNs. By connecting multiple flow cells to a single optimized converter, the area requirement is down to 1.26% while maintaining IRdrop below 5%. Experiments show that activity-based dynamic FCA switching extends by over 1.8× and 4.5× electrolytes lifetime for a processor duty-cycle of 50% and 20%, respectively.

The papers presented at ISVLSI 2020 explored emerging trends and novel ideas and concepts in the area of VLSI and brought the VLSI experience to new areas and technologies such as security, artificial intelligence and cyber-physical systems. A key area of emphasis of ISVLSI events is future design methodologies and new CAD tools. As in previous editions, the 2020 symposium brought together leading scientists and researchers from academia and industry.

Considering the reputation of the symposium, built over a period of three decades, it is no mean achievement to have a paper accepted and then selected as the Best Paper. Many congratulations to the EPFL authors for their singular academic triumph.


Best Paper winner (PDF)

1 Halima Najibi is pursuing her doctoral program in Electrical Engineering at EPFL

2 Alexandre Sébastien Julien Levisse is Scientist at ESL

3 Marina Zapater Sancho is currently Associate Professor in the REDS Institute but collaborates with ESL.

4 David Atienza is Associate Professor, ESL

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Dina G. Mahmoud Awarded CYD Doctoral Fellowship

Cyber-attacks can cause big damage: theft of sensitive data, failure of safety-critical equipment or disruption of vital communication networks. In January 2019, the Cyber Defence Campus (CYD) was established to anticipate and counter cyber threats. Since then, CYD has been promoting new solutions to major challenges in the fields of security and data science. In that context, CYD has joined forces with EPFL to launch the “CYD Fellowships – A Talent Program for Cyber-Defence Research.” The program recognizes and supports researchers by awarding Master Thesis, Doctoral and Distinguished Postdoctoral Fellowships. One of the first recipients of the CYD Doctoral Fellowship is Dina G. Mahmoud, Doctoral Assistant at EPFL’s School of Computer and Communication Sciences.

Dina first visited EPFL in summer 2018, as part of the Summer@EPFL internship program (1.9% acceptance rate). In 2019, Dina won an EPFL IC School Fellowship and entered the EDIC doctoral program. Her research, in collaboration with Mirjana Stojilović, focuses on fault attacks on FPGA-based platforms. Dina’s work was published at the “Design, Automation and Test in Europe” (DATE) conference in 2019 and the “Field-Programmable Logic and Applications” (FPL) conference in 2020.

The CYD doctoral fellowship will provide Dina with salary, research and travel expenses for three years (with a possible one-year extension). She was selected based on her strong background on hardware security and her motivation to engage with critical issues in cyber defence.

As a CYD Doctoral Fellow, Dina will conduct research on vulnerabilities and backdoors in heterogeneous hardware platforms, for protecting the confidentiality, integrity and availability of cyber and cyber-physical systems.

The CYD-EPFL joint initiative is expected to play a major role in encouraging the training of the next generation of leading researchers in cyber-security in Switzerland, of which Dina is a prime example. Apart from the Fellowship program to train young researchers, the CYD Campus also works to identify new developments in cyber space, promote technological and market monitoring and international scouting, and develop a collaboration network with industry leaders and academia.

We congratulate Dina G. Mahmoud on her award and hope that the CYD Fellowship will lead her to pioneering scientific achievements.

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EPFL Scientists Generate Laser-based Microwaves Using Built-in Photonic Chips

Microwave and radio signals play a pivotal role in radar technology and telecommunications including wireless networks. Moving away from the current tendency of using high-frequency bands for such applications, EPFL scientists have demonstrated laser-based microwave generators using built-in photonic chips developed at EPFL. This is an important breakthrough because high-frequency bands are prone to logjams because of high demand. On the other hand, microwave photonics offers high bandwidth, low transmission loss, and immunity to electromagnetic interference.

Microwave photonics, a combination of optoelectronics and microwave engineering, is built using optical frequency combs. Recently, a major advancement in this field was the development of chip-scale frequency combs from nonlinear microresonators fueled by continuous-wave lasers. These chip-scale frequency combs are often referred to as “soliton microcombs” because they depend on the development of ultra-short coherent light pulses called solitons.

In their study published in the Nature Photonics journal, EPFL researchers led by Tobias J. Kippenberg present integrated soliton microcombs that have repetition rates down to 10 GHz. They achieved this by significantly reducing the optical losses of integrated photonic waveguides based on silicon nitride, which is already being used in CMOS micro-electronic circuits. The silicon nitride waveguides produced by the researchers have the lowest loss recorded in any photonic integrated circuit. The resulting coherent soliton pulses have repetition rates in the microwave X-band (~10 GHz, utilized in radars) and the microwave K-band (~20 GHz, utilized in 5G network).

The microwave signals through this technology have phase noise characteristics that are on par or lower than that of electronic microwave synthesizers available on the market. By successfully demonstrating built-in soliton microcombs at microwave repetition rates, the research integrates the fields of microwave photonics, nonlinear optics, and integrated photonics.

The low optical losses achieved by the EPFL researchers allow light to spread nearly 1 meter in a waveguide that is only 1 micrometer in diameter, i.e., 100 times smaller than a human hair. The level of loss is the lowest seen in any closely limiting waveguide for integrated nonlinear photonics. The low loss is due to the innovative manufacturing technique called silicon nitride photonic Damascene process, devised by EPFL researchers.

The EPFL team is currently working with U.S. collaborators to create hybrid-integrated soliton microcomb modules. Such highly compact microcombs can be used in LiDAR, transceivers in datacenters, spectroscopy, microwave photonics, optical coherence tomography, and compact optical atomic clocks.

The research was funded by the Swiss National Science Foundation (SNF) and the Defense Advanced Research Projects Agency (DARPA).


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Google-Apple–based “SwissCovid” App in Pilot Test

The first coronavirus digital contact-tracing app using OS updates from Google and Apple is now in a large-scale pilot test. Dubbed as SwissCovid, the app is based on the decentralized protocol, where operations that have data privacy implications are not stored or conducted through a centralized server, but on the phone of individual users. Employees at EPFL, ETH Zurich, the Swiss Army, and select hospitals and cantonal administrations can now download the app for tracing contacts at risk of transmission of COVID-19.

Once downloaded and installed, the app signals to a user that he or she has been in prolonged contact with one or more people who subsequently tested positive for SARS-CoV-2. That not only helps the user take adequate precautions and test for the virus, but also checks the further spread of the disease.

The test, which was launched in late May, will clear the way for a roll out of the app to the public later this month after deliberations in the Swiss Parliament. However, recent research has indicated that 70% of Swiss residents approve the scheme. Professor Edouard Bugnion, Vice-President for Information Systems at EPFL and a key participant in the discussions with Google and Apple to have them adopt the “DP3T” protocol, said, “This is the first time that the operating system updates from Google and Apple enable its deployment and testing on such a large scale.” As observed by project manager Alfredo Sanchez, the Swiss testers now share a great responsibility because their usage could determine the widescale use of SwissCovid.

According to a report, 22 public health agencies have requested the API, while many countries might also switch to the Apple-Google framework because it offers “privacy by design,” minimizing the collection and sharing of information. In this context, Professor Carmela Troncoso, head of the Security & Privacy Engineering Laboratory at EPFL, said, “Our goal is to offer a solution that can be adopted in Europe and around the world…. There are millions of users and we owe it to them to be transparent.”

Although a pilot, the ongoing test phase is driven by real data. Eligible testers can opt to register for the pilot program, and any notification they receive through the app is not simulated. Therefore, they would be expected to take the advised precautions to check the spread of COVID-19.


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