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

Publications associées (33)

Cross-layer Exploration of 2.5D Energy-Efficient Heterogeneous Chiplets Integration: From System Simulation to Open Hardware

Anna Burdina, David Atienza Alonso, Giovanni Ansaloni, Miguel Peon Quiros, Pasquale Davide Schiavone

In the past decade, computing systems have significantly increased in complexity and power consumption. Nowadays, heterogeneous multi-processor systems-on-chip (MPSoCs) integrate many computing cores. Heterogeneous MPSoCs often comprise general-purpose pro ...

Association for Computing Machinery, Inc2024

Accelerator-driven Data Arrangement to Minimize Transformers Run-time on Multi-core Architectures

David Atienza Alonso, Giovanni Ansaloni, Alireza Amirshahi

The increasing complexity of transformer models in artificial intelligence expands their computational costs, memory usage, and energy consumption. Hardware acceleration tackles the ensuing challenges by designing processors and accelerators tailored for t ...

2024

Integrated Wireless Power, Data Communication, and Thermal Sensing System for Autonomous Multisite Brain Implants

Mohammad Javad Karimi

Implanted medical devices (IMDs) have been widely developed to support the monitoring and recording of biological data inside the body or brain. Wirelessly powered IMDs, a subset of implantable electronics, have been proposed to eliminate the limitations r ...

EPFL2024

REMOTE: Re-thinking Task Mapping on Wireless 2.5D Systems-on-Package for Hotspot Removal

David Atienza Alonso, Giovanni Ansaloni, Marina Zapater Sancho, Darong Huang, Rafael Medina Morillas

2.5D Systems-on-Package (SoPs) are composed by several chiplets placed on an interposer. They are becoming increasingly popular as they enable easy integration of electronic components in the same package and high fabrication yields. Nevertheless, they int ...

2023

Sub-kHz-Linewidth External-Cavity Laser (ECL) With Si3N4 Resonator Used as a Tunable Pump for a Kerr Frequency Comb

Tobias Kippenberg, Junqiu Liu

Combining optical gain in direct-bandgap III-V materials with tunable optical feedback offered by advanced photonic integrated circuits is key to chip-scale external-cavity lasers (ECL), offering wideband tunability along with low optical linewidths. Exter ...

IEEE Institute of Electrical and Electronics Engineers2023

Integrated electronics for time-of-flight positron emission tomography photodetectors

Andrada Alexandra Muntean

Positron emission tomography is a nuclear imaging technique well known for its use in oncology for cancer diagnosis and staging. A PET scanner is a complex machine which comprises photodetectors placed in a ring configuration that detect gamma photons gen ...

EPFL2023

Medipix4, a high granularity four sides buttable pixel readout chip for high resolution spectroscopic X-ray imaging at rates compatible with medical CT scans

Viros Sriskaran

Medipix4 is the latest member in the Medipix/Timepix family of pixel detector chips aimed at high rate spectroscopic X-ray imaging using high-Z materials. The chip address the limitations of conventional hybrid pixel detectors for X-ray imaging. Its predec ...

EPFL2022

Computational Imaging SPAD Cameras

Andrei Ardelean

Vision systems built around conventional image sensors have to read, encode and transmit large quantities of pixel information, a majority of which is redundant. As a result, new computational imaging sensor architectures were developed to preprocess the r ...

EPFL2023

Thermal and Power-Aware Run-Time Performance Management of 3D MPSoCs with Integrated Flow Cell Arrays

David Atienza Alonso, Marina Zapater Sancho, Giovanni Ansaloni, Alexandre Sébastien Julien Levisse, Halima Najibi

Flow Cell Arrays (FCA) technology employs microchannels filled with an electrolytic fluid to concurrently provide cooling and power generation to integrated circuits (ICs). This solution is particularly appealing for Three-Dimensional Multi-Processor Syste ...

2022

Deep Learning for Localized-Haptic Feedback in Tactile Surfaces

Camilo Hernandez Mejia

Touchscreens are nowadays the preferred choice for user interfaces in consumer electronics. Significant technological advances have been made in terms of touch sensing and visual quality. However, the haptic feedback offered by commercial products is still ...

EPFL2023

A Capacitively-Coupled Chopper Instrumentation Amplifier for Implantable Bridge Sensor Systems

Marco Mattavelli, Catherine Dehollain, Diego Ruben Barrettino, Kerim Türe, Mustafa Besirli

This paper presents an energy and area efficient capacitively-coupled chopper instrumentation amplifier (CCIA) dedicated to implantable bridge sensor systems. Chopper stabilization is employed to decrease its offset and 1/f noise and the resulting ripple d ...

IEEE2022

Robustness and invariance properties of image classifiers

Apostolos Modas

Deep neural networks have achieved impressive results in many image classification tasks. However, since their performance is usually measured in controlled settings, it is important to ensure that their decisions remain correct when deployed in noisy envi ...

EPFL2022

TiC-SAT: Tightly-coupled Systolic Accelerator for Transformers

David Atienza Alonso, Giovanni Ansaloni, Alireza Amirshahi, Joshua Alexander Harrison Klein

Transformer models have achieved impressive results in various AI scenarios, ranging from vision to natural language processing. However, their computational complexity and their vast number of parameters hinder their implementations on resource-constraine ...

2023

Regularity Matters: Designing Practical FPGA Switch-Blocks

Paolo Ienne, Stefan Nikolic

Several techniques have been proposed for automatically searching for FPGA switch-blocks which typically show some tangible advantage over the well-known academic architectures. However, the resulting switch-blocks usually exhibit high levels of irregulari ...

Assoc Computing Machinery2023

Understanding Deep Neural Function Approximation in Reinforcement Learning via ϵ-Greedy Exploration

Volkan Cevher, Fanghui Liu, Luca Viano

This paper provides a theoretical study of deep neural function approximation in reinforcement learning (RL) with the ϵ-greedy exploration under the online setting. This problem setting is motivated by the successful deep Q-networks (DQN) framework that fa ...

2022

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

Theory of Deep Learning: Neural Tangent Kernel and Beyond

Arthur Ulysse Jacot-Guillarmod

In the recent years, Deep Neural Networks (DNNs) have managed to succeed at tasks that previously appeared impossible, such as human-level object recognition, text synthesis, translation, playing games and many more. In spite of these major achievements, o ...

EPFL2022

Robust Training and Verification of Deep Neural Networks

Fabian Ricardo Latorre Gomez

According to the proposed Artificial Intelligence Act by the European Comission (expected to pass at the end of 2023), the class of High-Risk AI Systems (Title III) comprises several important applications of Deep Learning like autonomous driving vehicles ...

EPFL2023

Adaptive Body Biasing in Strong Body Factor Technologies

Thomas Christoph Müller

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