Wartungsankündigung: Wichtig: bitte beachten Sie unsere Wartungsankündigungen für Dienstag, den 02. April 2024 und Freitag, den 05. April 2024 auf der Magazineinstiegseite!
Maintenance announcement: please note our maintenance announcements for Tuesday, 02 April 2024 and Friday, 05 April on the repository page!
Wartungshinweis: wegen wichtigen Wartungsarbeiten an den OpenCast-Servern, bitten wir Sie über das Osterwochenende keine neuen Videos hochzuladen! Die bereits vorhandenen OpenCast-Videos stehen aber wieder zur Verfügung.
Symbol Kurs

convolutional neural networks for embedded systems

One of the problems for deploying convolutional neural networks(CNNs) on embedded systems has been large memory, power consumption, and computational complexity. In these networks, hundreds of filters and channels should be processed in high-dimensional convolutions. These computations cause a significant amount of data movement. There have been several proposed CNNs in order to make them suitable for embedded systems. Furthermore, there have been efforts in order to find a dataflow that supports parallel processing with minimal data movement cost in order to achieve a fast and energy-efficient CNN with the same accuracy. In this seminar, we will discuss the efforts that have been done to deploy the CNNs on embedded systems.

Zusammenfassung

One of the problems for deploying convolutional neural networks(CNNs) on embedded systems has been large memory, power consumption, and computational complexity. In these networks, hundreds of filters and channels should be processed in high-dimensional convolutions. These computations cause a significant amount of data movement. There have been several proposed CNNs in order to make them suitable for embedded systems. Furthermore, there have been efforts in order to find a dataflow that supports parallel processing with minimal data movement cost in order to achieve a fast and energy-efficient CNN with the same accuracy. In this seminar, we will discuss the efforts that have been done to deploy the CNNs on embedded systems.

Allgemein

Sprache
Deutsch
Copyright
This work has all rights reserved by the owner.

Verfügbarkeit

Zugriff
Unbegrenzt – wenn online geschaltet
Aufnahmeverfahren
Sie können diesem Kurs direkt beitreten.
Zeitraum für Beitritte
Unbegrenzt

Für Kursadministratoren freigegebene Daten

Daten des Persönlichen Profils
Benutzername
Vorname
Nachname
E-Mail
Matrikelnummer

Zusätzliche Informationen

Objekt-ID
1560782
Link zu dieser Seite