Wir haben unsere Datenschutzbestimmungen aktualisiert. Klicke hier, um dir die _Einzelheiten anzusehen. Tippe hier, um dir die Einzelheiten anzusehen.
Aktiviere deine kostenlose 30-tägige Testversion, um unbegrenzt zu lesen.
Erstelle deine kostenlose 30-tägige Testversion, um weiterzulesen.
Herunterladen, um offline zu lesen
Short
The growing amount of data captured by sensors and the real time constraints imply that not only big data analytics but also Machine Learning (ML) inference shall be executed at the edge. The multiple options for neural network acceleration in Arm-based platforms provide an unprecedented opportunity for new intelligent devices. It also raises the risk of fragmentation and duplication of efforts when multiple frameworks shall support multiple accelerators.
Andrea Gallo, Linaro VP of Segment Groups, will summarise the existing NN frameworks, accelerator solutions, and will describe the efforts underway in the Arm ecosystem.
Abstract
The dramatically growing amount of data captured by sensors and the ever more stringent requirements for latency and real time constraints are paving the way for edge computing, and this implies that not only big data analytics but also Machine Learning (ML) inference shall be executed at the edge. The multiple options for neural network acceleration in recent Arm-based platforms provides an unprecedented opportunity for new intelligent devices with ML inference. It also raises the risk of fragmentation and duplication of efforts when multiple frameworks shall support multiple accelerators.
Andrea Gallo, Linaro VP of Segment Groups, will summarise the existing NN frameworks, model description formats, accelerator solutions, low cost development boards and will describe the efforts underway to identify the best technologies to improve the consolidation and enable the competitive innovative advantage from all vendors.
Audience
The session will be useful for executives to engineers. Executives will gain a deeper understanding of the issues and opportunities. Engineers at NN acceleration IP design houses will take away ideas for how to collaborate in the open source community on their area of expertise, how to evaluate the performance and accelerate multiple NN frameworks without modifying them for each new IP, whether it be targeting edge computing gateways, smart devices or simple microcontrollers.
Benefits to the Ecosystem
The AI deep learning neural network ecosystem is starting just now and it has similar implications with open source as GPU and video accelerators had in the early days with user space drivers, binary blobs, proprietary APIs and all possible ways to protect their IPs. The session will outline a proposal for a collaborative ecosystem effort to create a common framework to manage multiple NN accelerators while at the same time avoiding to modify deep learning frameworks with multiple forks.
Short
The growing amount of data captured by sensors and the real time constraints imply that not only big data analytics but also Machine Learning (ML) inference shall be executed at the edge. The multiple options for neural network acceleration in Arm-based platforms provide an unprecedented opportunity for new intelligent devices. It also raises the risk of fragmentation and duplication of efforts when multiple frameworks shall support multiple accelerators.
Andrea Gallo, Linaro VP of Segment Groups, will summarise the existing NN frameworks, accelerator solutions, and will describe the efforts underway in the Arm ecosystem.
Abstract
The dramatically growing amount of data captured by sensors and the ever more stringent requirements for latency and real time constraints are paving the way for edge computing, and this implies that not only big data analytics but also Machine Learning (ML) inference shall be executed at the edge. The multiple options for neural network acceleration in recent Arm-based platforms provides an unprecedented opportunity for new intelligent devices with ML inference. It also raises the risk of fragmentation and duplication of efforts when multiple frameworks shall support multiple accelerators.
Andrea Gallo, Linaro VP of Segment Groups, will summarise the existing NN frameworks, model description formats, accelerator solutions, low cost development boards and will describe the efforts underway to identify the best technologies to improve the consolidation and enable the competitive innovative advantage from all vendors.
Audience
The session will be useful for executives to engineers. Executives will gain a deeper understanding of the issues and opportunities. Engineers at NN acceleration IP design houses will take away ideas for how to collaborate in the open source community on their area of expertise, how to evaluate the performance and accelerate multiple NN frameworks without modifying them for each new IP, whether it be targeting edge computing gateways, smart devices or simple microcontrollers.
Benefits to the Ecosystem
The AI deep learning neural network ecosystem is starting just now and it has similar implications with open source as GPU and video accelerators had in the early days with user space drivers, binary blobs, proprietary APIs and all possible ways to protect their IPs. The session will outline a proposal for a collaborative ecosystem effort to create a common framework to manage multiple NN accelerators while at the same time avoiding to modify deep learning frameworks with multiple forks.
Sie haben diese Folie bereits ins Clipboard „“ geclippt.
Sie haben Ihre erste Folie geclippt!
Durch Clippen können Sie wichtige Folien sammeln, die Sie später noch einmal ansehen möchten. Passen Sie den Namen des Clipboards an, um Ihre Clips zu speichern.Die SlideShare-Familie hat sich gerade vergrößert. Genießen Sie nun Zugriff auf Millionen eBooks, Bücher, Hörbücher, Zeitschriften und mehr von Scribd.
Jederzeit kündbar.Unbegrenztes Lesevergnügen
Lerne schneller und intelligenter von Spitzenfachleuten
Unbegrenzte Downloads
Lade es dir zum Lernen offline und unterwegs herunter
Außerdem erhältst du auch kostenlosen Zugang zu Scribd!
Sofortiger Zugriff auf Millionen von E-Books, Hörbüchern, Zeitschriften, Podcasts und mehr.
Lese und höre offline mit jedem Gerät.
Kostenloser Zugang zu Premium-Diensten wie TuneIn, Mubi und mehr.
Wir haben unsere Datenschutzbestimmungen aktualisiert, um den neuen globalen Regeln zum Thema Datenschutzbestimmungen gerecht zu werden und dir einen Einblick in die begrenzten Möglichkeiten zu geben, wie wir deine Daten nutzen.
Die Einzelheiten findest du unten. Indem du sie akzeptierst, erklärst du dich mit den aktualisierten Datenschutzbestimmungen einverstanden.
Vielen Dank!