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Publications associées (62)

Light-Intensity Switching of Graphene/WSe2 Synaptic Devices

Giulia Tagliabue, Tarique Anwar, Hongyu Tang

2D van der Waals heterojunctions (vdWH) have emerged as an attractive platform for the realization of optoelectronic synaptic devices, which are critical for energy-efficient computing systems. Photogating induced by charge traps at the interfaces indeed r ...

Wiley2024

HEEPocrates: An Ultra-Low-Power RISC-V Microcontroller for Edge-Computing Healthcare Applications

David Atienza Alonso, Alexandre Sébastien Julien Levisse, Miguel Peon Quiros, Simone Machetti, Pasquale Davide Schiavone

The field of edge computing in healthcare has seen remarkable growth due to the increasing demand for real-time processing of data in applications. However, challenges persist due to limitations in healthcare devices' performance and power efficiency. To o ...

Europractice2024

Acceleration of graph pattern mining and applications to financial crime

Jovan Blanusa

Various forms of real-world data, such as social, financial, and biological networks, can berepresented using graphs. An efficient method of analysing this type of data is to extractsubgraph patterns, such as cliques, cycles, and motifs, from graphs. For i ...

EPFL2023

A Delay and Power Efficient Voltage Level Shifter with Low Leakage Power

Alexandre Schmid, Mehdi Saberi

This paper presents a new power-efficient and high-speed voltage level shifter. In the proposed structure, the existing contentions at the internal nodes are reduced using auxiliary transistors and feedback networks, leading to a significant reduction in t ...

New York2023

Power Transformer Fault Diagnosis Using Neural Network Optimization Techniques

Dimitrios Kaminaris

Artificial Intelligence (AI) techniques are considered the most advanced approaches for diagnosing faults in power transformers. Dissolved Gas Analysis (DGA) is the conventional approach widely adopted for diagnosing incipient faults in power transformers. ...

Basel2023

Impact of Video Processing Operations in Deepfake Detection

Touradj Ebrahimi, Yuhang Lu

The detection of digital face manipulation in video has attracted extensive attention due to the increased risk to public trust. To counteract the malicious usage of such techniques, deep learning-based deepfake detection methods have been developed and ha ...

2023

Electrical-Level Attacks on CPUs, FPGAs, and GPUs: Survey and Implications in the Heterogeneous Era

Mirjana Stojilovic, Dina Gamaleldin Ahmed Shawky Mahmoud

Given the need for efficient high-performance computing, computer architectures combining CPUs, GPUs, and FPGAs are nowadays prevalent. However, each of these components suffers from electrical-level security risks. Moving to heterogeneous systems, with th ...

2022

Full System Exploration of On-Chip Wireless Communication on Many-Core Architectures

David Atienza Alonso, Marina Zapater Sancho, Giovanni Ansaloni, Rafael Medina Morillas, Yasir Mahmood Qureshi, Joshua Alexander Harrison Klein

In order to develop sustainable and more powerful information technology (IT) infrastructures, the challenges posed by the "memory wall" are critical for the design of high-performance and high-efficiency many-core computing systems. In this context, recen ...

2022

A 16-Channel 60µW Neural Synchrony Processor for Multi-Mode Phase-Locked Neurostimulation

Mahsa Shoaran, Uisub Shin, Cong Ding, Laxmeesha Somappa

Measuring neural oscillatory synchrony facilitates our understanding of complex brain networks and the underlying pathological states. Altering the cross-regional synchrony-as a measure of brain network connectivity-via phase-locked deep brain stimulation ...

IEEE2022

ALPINE: Analog In-Memory Acceleration with Tight Processor Integration for Deep Learning

David Atienza Alonso, Marina Zapater Sancho, Giovanni Ansaloni, Alexandre Sébastien Julien Levisse, Irem Boybat Kara, Yasir Mahmood Qureshi, Joshua Alexander Harrison Klein, Abu Sebastian

Analog in-memory computing (AIMC) cores offers significant performance and energy benefits for neural network inference with respect to digital logic (e.g., CPUs). AIMCs accelerate matrix-vector multiplications, which dominate these applications’ run-time. ...

2022

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