Publication

Computational memory-based inference and training of deep neural networks

Related publications (66)

About
Privacy
Disclaimer

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Computational memory-based inference and training of deep neural networks

Graph Chatbot

Chat with Graph Search

Error Tolerance Analysis of Deep Learning Hardware Using a Restricted Boltzmann Machine Toward Low-Power Memory Implementation

Social Scene Understanding: End-to-End Multi-Person Action Localization and Collective Activity Recognition

HRL: Efficient and Flexible Reconfigurable Logic for Near-Data Processing

Accelerating Machine Learning with Non-Volatile Memory: exploring device and circuit tradeoffs

Near-Memory Data Services

Memory-error tolerance of Scalable and Highly Parallel Architecture for Restricted Boltzmann Machines in Deep Belief Network

An MIG-based Compiler for Programmable Logic-in-Memory Architectures

Large-scale neural networks implemented with non-volatile memory as the synaptic weight element: impact of conductance response

Fast Logic Synthesis for RRAM-based In-Memory Computing using Majority-Inverter Graphs

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