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Publications associées (31)

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An Optimized Low-Power Band-Tuning TX for Short-Range FMCW Radar in 22-nm FDSOI CMOS

Christian Enz, Francesco Chicco, Sammy Cerida Rengifo

The paper presents a low-power transmitter (TX) as a part of a fully integrated 57-66 GHz FMCW radar system. The TX path includes a BPSK modulator and it is optimized for short-range operation with 0 dBm output power. A band-tuning technique is used for co ...

IEEE2021

Automated Design: A Journey Across Modelling, Optimization, and Education

Cyril Picard

Machine intelligence greatly impacts almost all domains of our societies. It is profoundly changing the field of mechanical engineering with new technical possibilities and processes. The education of future engineers also needs to adapt in terms of techni ...

EPFL2021

New Strategies for the Functionalization of Alkenes Exploiting Tethering Chemistry and Ring Strain

Bastian Antoine Rodolphe Claude Muriel

Amino alcohols, cyclopropanes and nitriles are privileged structural motifs found in the scaffold of natural products and bioactive compounds. In addition, they are versatile building blocks in organic chemistry. The development of new and increasingly eff ...

EPFL2021

Equinox: Training (for Free) on a Custom Inference Accelerator

Babak Falsafi, Martin Jaggi, Mario Paulo Drumond Lages De Oliveira, Ahmet Caner Yüzügüler, Arash Pourhabibi Zarandi, Louis Coulon

DNN inference accelerators executing online services exhibit low average loads because of service demand variability, leading to poor resource utilization. Unfortunately, reclaiming idle inference cycles is difficult as other workloads can not execute on a ...

ACM2021

Simulation Tool : Resources Management in High Performance Avionic for ADR Missions

Jean-Paul Richard Kneib, Michaël Yannick Juillard

This paper describes the second iteration in the development of a simulation tool that provides trade-offs of avionic architectures for future Active Debris Removal (ADR) space missions. ...

IEEE2021

Increasing Superstructure Optimization Capacity Through Self-Learning Surrogate Models

François Maréchal, Ivan Daniel Kantor, Julia Granacher

Simulation-based optimization models are widely applied to find optimal operating conditions of processes. Often, computational challenges arise from model complexity, making the generation of reliable design solutions difficult. We propose an algorithm fo ...

2021

Algebraic and Boolean Optimization Methods for AQFP Superconducting Circuits

Giovanni De Micheli, Heinz Riener, Eleonora Testa, Siang-Yun Lee

Adiabatic quantum-flux-parametron (AQFP) circuits are a family of superconducting electronic (SCE) circuits that have recently gained growing interest due to their low-energy consumption, and may serve as alternative technology to overcome the down-scaling ...

IEEE2021

Miss-Optimized Memory Systems: Turning Thousands of Outstanding Misses into Reuse Opportunities

Mikhail Asiatici

Even if Dennard scaling came to an end fifteen years ago, Moore' s law kept fueling an exponential growth in compute performance through increased parallelization. However, the performance of memory and, in particular, Dynamic Random Access Memory (DRAM), ...

EPFL2021

Optimizing Latent Space Directions For GAN-based Local Image Editing

Sabine Süsstrunk, Tong Zhang, Ehsan Pajouheshgar

Generative Adversarial Network (GAN) based localized image editing can suffer from ambiguity between semantic attributes. We thus present a novel objective function to evaluate the locality of an image edit. By introducing the supervision from a pre-traine ...

IEEE2021

Update on yesterday's dose-Use of delivery log-files for daily adaptive proton therapy (DAPT)

Marco Matter

In daily adaptive proton therapy (DAPT), the treatment plan is re-optimized on a daily basis. It is a straightforward idea to incorporate information from the previous deliveries during the optimization to refine this daily proton delivery. A feedback sign ...

2020

EEG Correlates of Difficulty Levels in Dynamical Transitions of Simulated Flying and Mapping Tasks

David Atienza Alonso, José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano, Adriana Arza Valdes, Fabio Isidoro Tiberio Dell'Agnola, Ping-Keng Jao

Decoding the subjective perception of task difficulty may help improve operator performance, i.e., automatically optimize the task difficulty level. Here, we aim to decode a compound of cognitive states that covaries with the task difficulty level. We desi ...

2020