Intel Corporation

Estelle Aflalo

Mattias Marder

Amit Bleiweiss

Eliran Zimmerman

Somdeb Majumdar

Shauharda Khadka

Hanlin Tang

Santiago Miret

Avrech Ben-David

Methods, apparatus, systems and articles of manufacture are disclosed to map workloads. An example apparatus includes a constraint definer to define performance characteristic targets of the neural network, an action determiner to apply a first resource configuration to candidate resources corresponding to the neural network, a reward determiner to calculate a results metric based on (a) resource performance metrics and (b) the performance characteristic targets, and a layer map generator to generate a resource mapping file, the mapping file including respective resource assignments for respective corresponding layers of the neural network, the resource assignments selected based on the results metric.

Methods, systems, articles of manufacture and apparatus to map workloads

Automatic multi-objective hardware optimization for processing a deep learning network is disclosed. An example of a storage medium includes instructions for obtaining client preferences for a plurality of performance indicators for processing of a deep learning workload; generating a workload representation for the deep learning workload; providing the workload representation to machine learning processing to generate a workload executable, the workload executable including hardware mapping based on the client preferences; and applying the workload executable in processing of the deep learning workload.

Automatic multi-objective hardware optimization for processing of deep learning networks

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Haifa, Israel

Estelle Aflalo