System-Level Exploration of In-Package Wireless Communication for Multi-Chiplet Platforms

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Adaptive Body Biasing in Strong Body Factor Technologies

With the advent of intelligent sensor nodes in everyday life, low power aspects of system design become more and more important. Adaptive body biasing is a promising methodology to achieve dynamic adaptation of the tradeoff between performance and energy b ...

EPFL2021

From Human-Designed Convolutional Neural Networks Towards Robust Neural Architecture Search

Kaicheng Yu

Artificial intelligence has been an ultimate design goal since the inception of computers decades ago. Among the many attempts towards general artificial intelligence, modern machine learning successfully tackles many complex problems thanks to the progres ...

EPFL2021

Defect segmentation for multi-illumination quality control systems

Pascal Fua, Engin Türetken, Siavash Arjomand Bigdeli, David Honzátko

Thanks to recent advancements in image processing and deep learning techniques, visual surface inspection in production lines has become an automated process as long as all the defects are visible in a single or a few images. However, it is often necessary ...

2021

Gem5-X : A Many-Core Heterogeneous Simulation Platform for Architectural Exploration and Optimization

David Atienza Alonso, William Andrew Simon, Marina Zapater Sancho, Yasir Mahmood Qureshi

The increasing adoption of smart systems in our daily life has led to the development of new applications with varying performance and energy constraints, and suitable computing architectures need to be developed for these new applications. In this paper, ...

2021

Generalization Comparison of Deep Neural Networks via Output Sensitivity

Patrick Thiran, Farnood Salehi, Mahsa Forouzesh

Although recent works have brought some insights into the performance improvement of techniques used in state-of-the-art deep-learning models, more work is needed to understand their generalization properties. We shed light on this matter by linking the lo ...

IEEE2021

Deep Image Restoration: Between Data Fidelity and Learned Priors

Majed El Helou

Image restoration reconstructs, as faithfully as possible, an original image from a potentially degraded version of it. Image degradations can be of various types, for instance haze, unwanted reflections, optical or spectral aberrations, or other physicall ...

EPFL2021

Chip-based soliton microcomb module using a hybrid semiconductor laser

Nicolas Volet, Rui Ning Wang, Tobias Kippenberg, Erwan Guillaume Albert Lucas, Junqiu Liu, Arslan Sajid Raja, Jijun He

Photonic chip-based soliton microcombs have shown rapid progress and have already been used in many system-level applications. There has been substantial progress in realizing soliton microcombs that rely on compact laser sources, culminating in devices th ...

2020

Embedded Microchannel Cooling for High Power-Density GaN-on-Si Power Integrated Circuits

Elison de Nazareth Matioli, Reza Soleiman Zadeh Ardebili, Georgios Kampitsis, Remco Franciscus Peter van Erp, Luca Nela

In this work, we demonstrate a new thermal management approach for direct cooling of GaN-on-Si power integrated circuits (ICs), in which the Si substrate functions as a microfluidic heat sink, turning Si into a cost-effective, high thermal performance subs ...

IEEE2020

Towards Deeply Scaled 3D MPSoCs with Integrated Flow Cell Array Technology

David Atienza Alonso, Mohamed Mostafa Sabry Aly, Halima Najibi, Marina Zapater Sancho, Alexandre Sébastien Julien Levisse

Deeply-scaled three-dimensional (3D) Multi-Processor Systems-on-Chip (MPSoCs) enable high performance and massive communication bandwidth for next-generation computing. However, as process nodes shrink, temperature-dependent leakage dramatically increases, ...

2020

Maximizing Fiber Cable Capacity Under A Supply Power Constraint Using Deep Neural Networks

Erixhen Sula

We experimentally achieve a 19% capacity gain per Watt of electrical supply power in a 12-span link by eliminating gain flattening filters and optimizing launch powers using deep neural networks in a parallel fiber context. (C) 2020 The Authors ...

IEEE2020

Multi-Objective Management of Multiprocessor Systems: From Heuristics to Reinforcement Learning

Arman Iranfar

In my thesis, I reveal several already-existing and emerging challenges in multi-objective management of multiprocessor systems, and address them through novel solutions, from heuristics to RL, depending on the complexity of the problem. Conventional multi ...

EPFL2020

Disentangling feature and lazy training in deep neural networks

Mario Geiger, Arthur Ulysse Jacot-Guillarmod, Matthieu Wyart, Stefano Spigler, Arthur Jacot

Two distinct limits for deep learning have been derived as the network width h -> infinity, depending on how the weights of the last layer scale with h. In the neural tangent Kernel (NTK) limit, the dynamics becomes linear in the weights and is described b ...

IOP PUBLISHING LTD2020

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