Intel Corporation

Soila Pertet Kavulya

Rita Chattopadhyay

Monica Lucia Martinez-Canales

Vinod Sharma

Malini Krishnan Bhandaru

Theodore L Willke

Peggy J Irelan

Kushal Datta

Xia Zhu

Bryn Keller

Michael R Alton

Abolfazl Shahbazi

Todd Lisonbee

Todd Lisobee

Briton L Barker

Technologies for performing sensor fusion include a compute device. The compute device includes circuitry configured to obtain detection data indicative of objects detected by each of multiple sensors of a host system. The detection data includes camera detection data indicative of a two or three dimensional image of detected objects and lidar detection data indicative of depths of detected objects. The circuitry is also configured to merge the detection data from the multiple sensors to define final bounding shapes for the objects.

Technologies for fusing data from multiple sensors to improve object detection, identification, and localization

An apparatus for optimizing a skewed join is described herein. The apparatus includes logic, at least partially including hardware logic, to determine that a dataset for the skewed join comprises a skewed key that does not fit in memory. The apparatus also includes logic to model the skewed join as a queue. The apparatus also includes logic to estimate a cost of the skewed join based on the modeled queue. The apparatus evaluates different join techniques, and partitioning strategies for the skewed join, and chooses the plan with the lowest cost.

Optimizing skewed joins in big data

Various systems and methods for implementing context-based digital signal processing are described herein. An object detection system includes a processor to: access sensor data from a first sensor and a second sensor integrated in a vehicle; access an operating context of the vehicle; assign a first weight to a first object detection result from sensor data of the first sensor, the first weight adjusted based on the operating context; assign a second weight to a second object detection result from sensor data of the second sensor, the second weight adjusted based on the operating context; and perform a combined object detection technique by combining the first object detection result weighted by the first weight and the second object detection result weighted by the second weight.

Context-based digital signal processing

A system and method for orchestrating a table operation of data with a graph operation of the data using columnar stores. The orchestration includes storing vertices and edges as collections of tables by type, and supporting the columnar stores with different storage characteristics. The techniques may also include a graph query optimizer that combines chained operators of a graph query; and/or the graph query executed via an in-memory distributed query execution engine.

Graph operations

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Hillsboro, OR, United States of America

Soila Pertet Kavulya