Publications related to Design and Implementation of a Neural Network Aided Self Interference Cancellation Scheme for Full-Duplex Radios

Ultra-Low Power Short-Range 60-GHz FMCW Radar Front-End

The millimeter wave (mmWave) frequency band is becoming very interesting over the past years because it provides the opportunity to operate using large bandwidths which are not available at lower frequencies. This enables new communication standards suppor ...

EPFL2023

Topological MIMO Optical Signal Processing

Romain Christophe Rémy Fleury, Farzad Zangeneh Nejad

An important advantage of optical signal processing is the possibility of performing various operations in parallel. Among the techniques proposed for parallel optical signal processing, frequency division multiplexing (FDM) is a well-known approach, in wh ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Hardware-Software Co-design for Improved Resource Utilization in DNN Accelerators

Ahmet Caner Yüzügüler

Deep neural networks (DNN) have become an essential tool to tackle challenging tasks in many fields of computer science. However, their high computational complexity limits their applicability. Specialized DNN accelerators have been developed to accommodat ...

EPFL2023

Intelligent RF System for Ultra Low Power Spectrum Sensing with Machine Learning

Naci Pekçokgüler

The crowdedness of the RF spectrum constitutes problems in communication, however it also accommodates large amount of useful information. This information can be used to determine existing signals and operating contexts. However, spectrum condition varies ...

EPFL2024

Building Chips Faster: Hardware-Compiler Co-Design for Accelerated RTL Simulation

Sahand Kashani

The demise of Moore's Law and Dennard scaling has resulted in diminishing performance gains for general-purpose processors, and so has prompted a surge in academic and commercial interest for hardware accelerators. Specialized hardware has already redefine ...

EPFL2023

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning

Jean-Philippe Thiran, Roser Vinals Terres

Ultrafast ultrasound imaging, characterized by high frame rates, generates low-quality images. Convolutional neural networks (CNNs) have demonstrated great potential to enhance image quality without compromising the frame rate. However, CNNs have been most ...

2023

Structured and tiled-based pruning of Deep Learning models targeting FPGA implementations

Alexandre Schmid, Lizeth Gonzalez Carabarin

Model compression techniques have lead to a reduction of size and number of computations of Deep Learning models. However, techniques such as pruning mostly lack of a real co-optimization with hardware platforms. For instance, implementing unstructured pru ...

IEEE2022

Beam Selection and Tracking for Amplify-and-Forward Repeaters

Ruben Merz, Andreas Peter Burg, Adrian Schumacher

Mobile network operators constantly have to upgrade their cellular network to satisfy the public's hunger for increasing data capacity. However, regulatory limits regarding allowed electromagnetic field strength on existing cell sites often limit or preven ...

IEEE2022

In-Memory Hardware and Architectural Extensions for Workloads Acceleration

William Andrew Simon

Utilization of edge devices has exploded in the last decade, with such use cases as wearable devices, autonomous driving, and smart homes. As their ubiquity grows, so do expectations of their capabilities. Simultaneously, their formfactor and use cases lim ...

EPFL2022

New Technologies to Enhance the Figures-of-Merit of GaN Power Devices

Luca Nela

Gallium Nitride (GaN) has enabled groundbreaking developments in the field of optoelectronics and radio frequency communication. More recently, GaN devices for power conversion applications have demonstrated excellent potential. Thanks to Gallium Nitride w ...

EPFL2022

Cellular Fronthauling for Data Capacity Increase in Underserved Spaces

Adrian Schumacher

The increase in wireless data traffic continues and is a product of several factors. First, new technologies and capabilities enable new use cases for which new products emerge. Then, with the growing user adoption over time, the data traffic is further in ...

EPFL2022

A technique for non-intrusive greedy piecewise-rational model reduction of frequency response problems over wide frequency bands

Fabio Nobile, Davide Pradovera

In the field of model order reduction for frequency response problems, the minimal rational interpolation (MRI) method has been shown to be quite effective. However, in some cases, numerical instabilities may arise when applying MRI to build a surrogate mo ...

2022

Decentralized in-order execution of a sequential task-based code for shared-memory architectures

Charly Nicolas Lucien Castes

The hardware complexity of modern machines makes the design of adequate programming models crucial for jointly ensuring performance, portability, and productivity in high-performance computing (HPC). Sequential task-based programming models paired with adv ...

IEEE COMPUTER SOC2022

Error Resilient In-Memory Computing Architecture for CNN Inference on the Edge

David Atienza Alonso, Giovanni Ansaloni, Alexandre Sébastien Julien Levisse, Flavio Ponzina, Marco Antonio Rios

The growing popularity of edge computing has fostered the development of diverse solutions to support Artificial Intelligence (AI) in energy-constrained devices. Nonetheless, comparatively few efforts have focused on the resiliency exhibited by AI workload ...

2022

High-frequency broadband laser phase noise cancellation using a delay line

Timo Zwettler

Laser phase noise remains a limiting factor in many experimental settings, including metrology, time-keeping, as well as quantum optics. Hitherto this issue was addressed at low frequencies ranging from well below 1 Hz to maximally 100 kHz. However, a wide ...

2021

Networks of Coupled VO2 Oscillators for Neuromorphic Computing

Elisabetta Corti

Neuromorphic computing is a wide research field aimed to the realization of brain-inspired hardware, apt to tackle computation of unstructured data more efficiently than currently done with standard computational units. Oscillatory neural networks are know ...

EPFL2021

Exploring MEG brain fingerprints: Evaluation, pitfalls, and interpretations

Dimitri Nestor Alice Van De Ville, Alessandra Griffa, Enrico Amico, Sélima Zahar, Ekansh Sareen

Individual characterization of subjects based on their functional connectome (FC), termed “FC fingerprinting”, has become a highly sought-after goal in contemporary neuroscience research. Recent functional magnetic resonance imaging (fMRI) studies have dem ...

2021

Hardware Implementation of Neural Self-Interference Cancellation

Andreas Peter Burg, Alexios Konstantinos Balatsoukas Stimming, Yann Kurzo, Andreas Toftegaard Kristensen

In-band full-duplex systems can transmit and receive information simultaneously and on the same frequency band. However, due to the strong self-interference caused by the transmitter to its own receiver, the use of non-linear digital self-interference canc ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Physical Layer Aspects of LoRa and Full-Duplex Wireless Transceivers

Orion Afisiadis

Wireless communications are currently faced with two main challenges. The first challenge stems from the enormous number of Internet of Things (IoT) devices that transmit very small amounts of data. The second challenge is the need for ever-increasing data ...

EPFL2020

On the Implementation Complexity of Digital Full-Duplex Self-Interference Cancellation

Andreas Peter Burg, Alexios Konstantinos Balatsoukas Stimming, Andreas Toftegaard Kristensen

In-band full-duplex systems promise to further increase the throughput of wireless systems, by simultaneously transmitting and receiving on the same frequency band. However, concurrent transmission generates a strong self-interference signal at the receive ...

IEEE2020