Intel Corporation

Shengtian Zhou

Justin E Gottschlich

Mohammad Mejbah Ul Alam

Alexander F Heinecke

Jeffrey Ota

Sara S Baghsorkhi

David Israel Gonzalez Aguirre

Ignacio Javier Alvarez

Jason Martin

Niranjan Hasabnis

Celine Lee

Bahareh Sadeghi

Mats Gustav Agerstam

Javier Felip Leon

Marcos Emanuel Carranza

Evangelos Georganas

Luis Carlos Maria Remis

Maria E Ramirez Loaiza

Jesmin Jahan Tithi

Kathiravetpillai Sivanesan

Hassnaa Moustafa

Liuyang L Yang

Sridhar G Sharma

Javier Sebastian Turek

Paul M Petersen

Li Chen

Robert Colby

Cesar Martinez-Spessot

Patrick Mead

Vasileios Porpodas

Nesreen K Ahmed

Javier Sebastián Turek

Anand Venkat

Mihai Capota

Fangke Ye

Guixiang Ma

Emine Tatbul Bitim

Roshni Iyer

Methods, systems, and apparatus for automatic detection of software bugs are disclosed. An example apparatus includes a comparator to compare reference code to input code to detect a source code error in the input code; a graph generator to generate a graphical representation of the reference code or the input code, the graphical representation to identify non-overlapping code regions; and a root cause determiner to determine a root cause of the source code error in the input code, the root cause based on the non-overlapping code regions.

Methods and apparatus for automatic detection of software bugs

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, apparatus, systems, and articles of manufacture are disclosed herein that mitigate hard-braking events. An example apparatus at least one memory; instructions; and processor circuitry to execute the instructions to: determine a danger level associated with an object, the danger level indicative of a first measure of damage corresponding to a trajectory of the object compared to a trajectory of a vehicle; determine, based on the first danger level, a danger measure based on at least one of a position of the object, a velocity of the object, an acceleration of the object, a direction of travel of the object, a weight or mass of the object; and generate instructions to transmit to a steering system or a braking system of the vehicle based on the determination.

Methods and apparatus to mitigate hard-braking events

Methods, apparatus, systems, and articles of manufacture to improve data quality for artificial intelligence are disclosed. An example apparatus includes an interface; instructions; and processor circuitry to execute the instruction to: determine an indirect quality of a repository that include datapoints of a dataset; determine a direct quality of the repository that include the datapoints of the dataset; determine a dataset quality based on the indirect quality of the repository and the direct quality of the repository; and when the quality does not satisfy a threshold, filter out a subset of the datapoints to prepare the dataset to support the training of the neural network.

Methods and apparatus to improve data quality for artificial intelligence

Methods, apparatus, systems, and articles of manufacture are disclosed that implement an automatically evolving code recommendation engine. In one example, the apparatus collects a user code snippet. The apparatus then determines a structured representation of the user code snippet. Next, the apparatus generates a recommended code snippet using the structured representation of the user code snippet. Then the apparatus obtains user-determined code snippet feedback comparing the user code snippet to the recommended code snippet, the user-determined code snippet feedback indicating one of a match, no match, or uncertain. Finally, the apparatus stores a code snippet training pair in a training database, the code snippet training pair including the user code snippet and the recommended code snippet.

Methods and apparatus to automatically evolve a code recommendation engine

Methods, apparatus, systems and articles of manufacture to identify a side-channel attack are disclosed. Example instructions cause one or more processors to generate an event vector based on one or more counts corresponding to tasks performed by a central processing unit; determine distances between the event vector and weight vectors of neurons in a self-organizing map; select a neuron of the neurons that results based on a determined distance; identify neurons that neighbor the selected neuron; and update at least one of a weight vector of the selected neuron or weight vectors of the neighboring neurons based on the determined distance of the selected neuron.

Methods and apparatus to detect side-channel attacks

Methods, apparatus, systems, and articles of manufacture are disclosed to determine refined context for bug detection. At least one non-transitory machine-readable medium includes instructions that, when executed, cause at least one processor to at least classify a node on a graph, the graph to represent a computer program, the node to contain partial bug context corresponding to the computer program; identify a location of a software bug in the computer program, the location based on the node; determine a static bug context of the software bug using the location of the software bug; determine a dynamic bug context of the software bug using the location of the software bug; and determine a refined bug context based on a merge of the static bug context and the dynamic bug context.

Methods and apparatus to determine refined context for software bug detection and correction

An example apparatus comprises a transformation generator to generate a population of code variants corresponding to an input code, the population of code variants to include transformation sequences of the input code, a dependence analyzer to analyze the population of code variants for dependence vectors, a profile controller to profile the population of code variants to determine performance metrics of hardware during an execution of respective ones of the transformation sequences, and a hash code generator to generate hash codes for storing in a database, the hash codes (a) corresponding to a combination of the dependence vectors and respective performance metrics and (b) mapped to respective transformation sequences.

Methods, systems, articles of manufacture and apparatus to automatically optimize software programs

Methods, apparatus, systems, and articles of manufacture are disclosed herein that mitigate hard-braking events. An example apparatus includes a world generator to generate a deep learning model to identify and categorize an object in a proximity of a vehicle, a data analyzer to determine a danger level associated with the object, the danger level indicative of a likelihood of a collision between the vehicle and the object, a vehicle response determiner to determine, based on the danger level, a response of the vehicle to avoid a collision with the object, and an instruction generator to transmit instructions to a steering system or a braking system of the vehicle based on the determined vehicle response.

Methods, apparatus, systems and articles of manufacture are disclosed that provide an apparatus to analyze vehicle perspectives, the apparatus comprising a profile generator to generate a first profile of an environment based on a profile template and first data generated by a first vehicle; a data analyzer to: determine a difference between the first profile and a second profile obtained from a first one of one or more nodes in the environment; and in response to a trigger event, update the first profile based on the difference; and a vehicle control system to: in response to the trigger event, update a first perspective of the environment based on one or more of second data from the first one of the one or more nodes or the updated first profile; update a path plan for the first vehicle based on the updated first perspective; and execute the updated path plan.

Methods and apparatus to update autonomous vehicle perspectives

Methods, apparatus, systems and articles of manufacture are disclosed that adjust autonomous vehicle driving software using machine programming. An example apparatus for adjusting autonomous driving software of a vehicle includes an input analyzer to determine a software adjustment based on an obtained driving input and a priority determiner to determine a priority level of the software adjustment. The apparatus further includes a program adjuster to, when the priority level is above a threshold, identify a parameter of the autonomous driving software of the vehicle associated with the software adjustment and adjust the parameter based on the software adjustment, the adjustment to the parameter to change driving characteristics of the vehicle.

Methods and apparatus to adjust autonomous vehicle driving software using machine programming

Systems and methods for determining a configuration for a microarchitecture are described herein. An example system includes a proposal generator to generate a first candidate configuration of parameters for the microarchitecture, a machine learning model to process the first candidate configuration of parameters to output estimated performance indicators for the microarchitecture, an uncertainty checker to determine whether the estimated performance indicators are reliable, and a performance checker. In response to a determination that the estimated performance indicators are reliable, the performance checker is to determine whether the estimated performance indicators have improved toward a target. Further, if the estimated performance indicators have improved, the performance checker is to store the first candidate configuration of parameters in a memory as a potential solution for a microarchitecture without performing a full simulation on the first candidate configuration of parameters.

Systems and methods for determining a configuration for a microarchitecture

Apparatus, systems, articles of manufacture, and methods for a context and complexity-aware recommendation system for efficient software development. An example apparatus includes a current state generator to generate a representation of a current state of a new function, an instruction predictor to generate a first recommended software component based on the current state of the new function, a complexity cost determiner to rank the first recommended software component based on a weighted sum of associated partial cost values, the software component to be ranked against second recommended software components based on a comparison of partial cost values corresponding to respective ones of the second recommended software components, a risk identifier to detect vulnerabilities based on an attack surface of a portion of the first recommended software component, and a ranking determiner to generate a third recommended software component, the third recommended software component corresponding to respective ranking metrics.

Methods, systems, articles of manufacture, and apparatus for a context and complexity-aware recommendation system for improved software development efficiency

Methods, apparatus, systems and articles of manufacture are disclosed for code review assistance for dynamically typed languages. An example apparatus to analyze a segment of code includes a function identifier to identify a first input of a first function call included in the segment of the code, a parameter type vector (PTV) estimator model to estimate a first data structure based on the first input, the PTV estimator model generated via a set of reviewed code, a PTV determiner to generate a second data structure based on a data parameter type of the first input, an error comparator to determine a first reconstruction error based on the first data structure, and the second data structure and a recommendation generator to, if the first reconstruction error does not satisfy a recommendation threshold, generate a first recommendation to review the first function call.

Methods, systems, articles of manufacture and apparatus for code review assistance for dynamically typed languages

Methods, apparatus, systems and articles of manufacture to identify a side-channel attack are disclosed. An example apparatus includes a vector-to-neuron processor to map an event vector to a neuron of a trained self-organizing map; a buffer processor to identify a task pair based on the neuron and an adjacent neuron of the neuron; a buffer to store data corresponding to the identified task pair; an attack identifier to, when information stored in the buffer corresponds to more than a threshold number of task pairs corresponding to the identified task pair, identify a malware attack; and a mitigation technique selector to select a technique for mitigating the malware attack.

Growing community of inventors

Palo Alto, CA, United States of America

Shengtian Zhou