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Publications associées (32)

Page 2 sur 2

A Pixel Design of a Branching Ultra-Highspeed Image Sensor

Edoardo Charbon

A burst image sensor named Hanabi, meaning fireworks in Japanese, includes a branching CCD and multiple CMOS readout circuits. The sensor is backside-illuminated with a light/charge guide pipe to minimize the temporal resolution by suppressing the horizont ...

2021

Running Efficiently CNNs on the Edge Thanks to Hybrid SRAM-RRAM In-Memory Computing

David Atienza Alonso, Giovanni Ansaloni, Alexandre Sébastien Julien Levisse, Flavio Ponzina, Marco Antonio Rios

The increasing size of Convolutional Neural Networks (CNNs) and the high computational workload required for inference pose major challenges for their deployment on resource-constrained edge devices. In this paper, we address them by proposing a novel In-M ...

IEEE2021

Synthesis, structural and magnetic characterization of thin films of the chiral magnets CoZnMn and FeGe

Anna Kúkol'ová

Skyrmions are topologically protected nanometer-sized magnetic vortices that have sparked interest for future spintronics applications aiming at high-density, high-speed and low-power consuming magnetic memory devices. One type of skyrmions appears in non- ...

EPFL2021

Software Support for Non-Volatile Memory (NVM) Programming

David Teksen Aksun

Non-Volatile Memory (NVM) is an emerging type of memory device that provides fast, byte-addressable, and high-capacity durable storage. NVM sits on the memory bus and allows durable data structures designs similar to the in-memory equivalent ones. Expensiv ...

EPFL2021

Accurate Emulation of Memristive Crossbar Arrays for In-Memory Computing

Irem Boybat Kara

In-memory computing is an emerging non-von Neumann computing paradigm where certain computational tasks are performed in memory by exploiting the physical attributes of the memory devices. Memristive devices such as phase-change memory (PCM), where informa ...

IEEE2020

Accurate deep neural network inference using computational phase-change memory

Irem Boybat Kara, Abu Sebastian

In-memory computing using resistive memory devices is a promising non-von Neumann approach for making energy-efficient deep learning inference hardware. However, due to device variability and noise, the network needs to be trained in a specific way so that ...

Nature Research2020

Perovskite Flash Memory with a Single-Layer Nanofloating Gate

Mohammad Khaja Nazeeruddin, Peng Gao, Tom Theodorus Antonius Lummen, Peng Gao, Hobeom Kim

Here we use triple-cation metal-organic halide perovskite single crystals for the transistor channel of a flash memory device. Moreover, we design and demonstrate a 10 nm thick single-layer nanofloating gate. It consists of a ternary blend of two organic s ...

2020

Gain-Cell Embedded DRAMs: Modeling and Design Space

Andreas Peter Burg, Robert Giterman, Andrea Bonetti, Adam Shmuel Teman

Among the different types of DRAMs, gain-cell embedded DRAM (GC-eDRAM) is a compact, low-power and CMOS-compatible alternative to conventional SRAM. GC-eDRAM achieves high memory density as it relies on a storage cell that can be implemented with as few as ...

IEEE2020

Distributed Computing with Modern Shared Memory

Mihail Igor Zablotchi

In this thesis, we revisit classic problems in shared-memory distributed computing through the lenses of (1) emerging hardware technologies and (2) changing requirements. Our contributions consist, on the one hand, in providing a better understanding of th ...

EPFL2020

Gain-Cell Embedded DRAMs: Modeling and Design Space

Andreas Peter Burg, Robert Giterman, Andrea Bonetti, Adam Shmuel Teman

Among the different types of dynamic random-access memories (DRAMs), gain-cell embedded DRAM (GC-eDRAM) is a compact, low-power, and CMOS-compatible alternative to conventional static random-access memory (SRAM). GC-eDRAM achieves high memory density, as i ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Intrinsic or nucleation-driven switching: An insight from nanoscopic analysis of negative capacitance Hf1-xZrxO2-based structures

Mihai Adrian Ionescu, Igor Stolichnov, Matteo Cavalieri, Carlotta Gastaldi

HfO2-based ferroelectrics have dramatically changed the application perspectives of polarization-switching materials for information processing and storage. Their CMOS compatibility and preservation of high reversible polarization down to a few nanometer t ...

AMER INST PHYSICS2020

Logic-in-memory based on an atomically thin semiconductor

Aleksandra Radenovic, Andras Kis, Ahmet Avsar, Zhenyu Wang, Yanfei Zhao, Mukesh Kumar Tripathi, Guilherme Migliato Marega

The growing importance of applications based on machine learning is driving the need to develop dedicated, energy-efficient electronic hardware. Compared with von Neumann architectures, which have separate processing and storage units, brain-inspired in-me ...

2020