Publication

Experimental Demonstration and Tolerancing of a Large-Scale Neural Network (165 000 Synapses) Using Phase-Change Memory as the Synaptic Weight Element

Publications associées (41)

Graph Chatbot

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

Connectez-vous pour utiliser Chat avec Graph Search

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Scott William Pesme

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

2

-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

EPFL2024

Experimental Demonstration and Tolerancing of a Large-Scale Neural Network (165 000 Synapses) Using Phase-Change Memory as the Synaptic Weight Element

Graph Chatbot

Chattez avec Graph Search

Deep Learning Theory Through the Lens of Diagonal Linear Networks

2D Nanosystems: Applications of 2D Semiconductors for In-Memory Computing

Predicting nonlinear optical scattering with physics-driven neural networks

Impact of Interface Traps in Floating-Gate Memory Based on Monolayer MoS2

In-Memory Hardware and Architectural Extensions for Workloads Acceleration

Face Reconstruction Fromdeep Facial Embeddings Using A Convolutional Neural Network

Mixed-Precision Deep Learning Based on Computational Memory

Accurate Emulation of Memristive Crossbar Arrays for In-Memory Computing

Disentangling feature and lazy training in deep neural networks

Accurate deep neural network inference using computational phase-change memory

2D Nanosystems: Applications of 2D Semiconductors for In-Memory Computing

Predicting nonlinear optical scattering with physics-driven neural networks

Deep Learning Theory Through the Lens of Diagonal Linear Networks

In-Memory Hardware and Architectural Extensions for Workloads Acceleration

Mixed-Precision Deep Learning Based on Computational Memory

Accurate Emulation of Memristive Crossbar Arrays for In-Memory Computing

Face Reconstruction Fromdeep Facial Embeddings Using A Convolutional Neural Network

Disentangling feature and lazy training in deep neural networks

Accurate deep neural network inference using computational phase-change memory

Impact of Interface Traps in Floating-Gate Memory Based on Monolayer MoS2