Intel Corporation

Nesreen K Ahmed

Mihai Capota

Nageen Himayat

Giuseppe Beppe Raffa

Ignacio Javier Alvarez

Ravikumar Balakrishnan

Theodore L Willke

Shengtian Zhou

Mariano Tepper

Niranjan Hasabnis

Vy Vo

Bryn Keller

Hesham Mostafa

Guixiang Ma

Various examples relate to an apparatus, device, method, and computer program for determining a placement of an execution of a computer program, and to an apparatus, device, method, and computer program for training at least one machine-learning model. The apparatus for determining the placement of an execution of a computer program comprises processing circuitry that is configured to generate a graph representation of a computer program, generate, using a first machine-learning model, a vector embedding of the graph representation of the computer program, and to determine, based on an output of a second machine-learning model, a placement of an execution of the computer program on one or more hardware devices of a heterogenous plurality of hardware devices of a computer system, with the vector embedding and information on the load of the hardware devices being provided as input to the second machine-learning model.

Execution placement of a computer program using a trained machine-learning model

Methods, systems, and computer programs are presented for implementing Personalized Mobility as a Service (PMaaS) to improve transportation services delivery. One storage medium includes instructions for detecting, by a mobility as a service (MaaS) system, a request for a trip from a user device of a user. The storage medium further includes instructions for mapping, using a model executing on the machine, the user to a persona from a plurality of persona models. Each persona model has one or more characteristics associated with users of the MaaS system. Further yet, the storage medium includes instructions for determining trip parameters for the trip based on the persona mapped to the user, the trip parameters defining one or more trip segments for the trip, and instructions for providing trip parameters to the user device.

Personalized mobility as a service

Systems, apparatuses and methods may provide for technology that generates a dependence graph based on a plurality of intermediate representation (IR) code instructions associated with a compiled program code, generates a set of graph embedding vectors based on the plurality of IR code instructions, and determines, via a neural network, one of an analysis of the compiled program code or an enhancement of the program code based on the dependence graph and the set of graph embedding vectors. The technology may provide a graph attention neural network that includes a recurrent block and at least one task-specific neural network layer, the recurrent block including a graph attention layer and a transition function. The technology may also apply dynamic per-position recurrence-halting to determine a number of recurring steps for each position in the recurrent block based on adaptive computation time.

System to analyze and enhance software based on graph attention networks

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Santa Clara, CA, United States of America

Nesreen K Ahmed