Publications associées à In-Memory Sensing and Computing for Cancer Diagnostics: A Perspective Paper

Publications associées (8)

Stop Wasting your Cache! Bringing Machine Learning into Cache Computing

The rapid evolution of Machine Learning (ML) workloads, particularly Deep Neural Networks (DNNs) and Transformer-based models, has intensified demands on computing architectures, highlighting the limitations of traditional von Neumann systems due to the me ...

ACM2025

Electronic metadevices for terahertz applications

Elison de Nazareth Matioli, Mohammad Samizadeh Nikooytabalvandani

The evolution of electronics has largely relied on downscaling to meet the continuous needs for faster and highly integrated devices(1). As the channel length is reduced, however, classic electronic devices face fundamental issues that hinder exploiting ma ...

NATURE PORTFOLIO2023

Non-Structured DNN Weight Pruning--Is It Beneficial in Any Platform?

Shaokai Ye

Large deep neural network (DNN) models pose the key challenge to energy efficiency due to the significantly higher energy consumption of off-chip DRAM accesses than arithmetic or SRAM operations. It motivates the intensive research on model compression wit ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Associativity-agnostic in-cache computing memory architecture optimized for multiplication

David Atienza Alonso, Marina Zapater Sancho, Alexandre Sébastien Julien Levisse, Marco Antonio Rios

A random access memory array including a plurality of local memory group ways, each local memory group way including, a plurality of local memory groups, each local memory group including, a memory column including a plurality of memory cells, a pair of lo ...

2021

Analog signal processing with topological metamaterials

Farzad Zangeneh Nejad

Originally discovered in condensed matter systems, topological insulators (TIs) have been ubiquitously extended to various fields of classical wave physics including photonics, phononics, acoustics, mechanics, and microwaves. In the bulk, like any other in ...

EPFL2021

Introducing a biomimetic coating for graphene neuroelectronics: toward in-vivo applications

Grégoire Courtine, Quentin Barraud, Marco Bonizzato, Elodie Rey

Electronic micro and nano-devices are suitable tools to monitor the activity of many individual neurons over mesoscale networks. However the inorganic materials currently used in microelectronics are barely accepted by neural cells and tissues, thus limiti ...

IOP PUBLISHING LTD2021

New Reliability Assessment of MEMS Components under Accumulative Testing for Space Applications

Maxime Auchlin

This dissertation proposes a novel accumulative test method for MEMS-based, commercial off-the-shelves inertial measure units (IMUs) from the automotive field. The need for clarification and augmentation of the corpus of knowledge on MEMS reliability for s ...

EPFL2021

Output-Capacitance Hysteresis Losses of Field-Effect Transistors

Elison de Nazareth Matioli, Armin Jafari, Luca Nela, Palliyage Srilak Nirmana Perera, Georgios Kampitsis

Resonant-type power converters are supposed to generate zero switching losses during soft-switching operation. In recent research, unexpected switching losses were reported in these converters, which were attributed to a large-signal hysteresis observed in ...

IEEE2020