Intel Corporation

At&t Intellectual Property I, L.p.

Rensselaer Polytechnic Institute

At+t Corp.

Omesh Tickoo

Srenivas Varadarajan

Vallabhajosyula S Somayazulu

Ibrahima J Ndiour

Yiting Liao

Shao-Wen Yang

Yen-kuang Chen

Ravishankar Iyer

Glen J Anderson

Rajesh Poornachandran

Myung Hwangbo

Teahyung Lee

Kadangode K Ramakrishnan

Willem M Beltman

Mahesh M Subedar

Kathy Yuen

Vinayak Honkote

Nilesh Amar Ahuja

Shivkumar Kalyanaraman

Vijaynarayanan Subramanian

Krishna Kumar Singh

Parual Datta

Rita H Wouhaybi

Shilpa Talwar

David I Poisner

Michael E Kounavis

John Charles Weast

Jonathan J Huang

Javier Felip Leon

Stanley T Mo

Javier Perez-Ramirez

David Gomez Gutierrez

Maynard C Falconer

Mark R Francis

Ranganath Krishnan

Andradige Pubudu Madhawa Silva

Nikita Tiwari

Erin Carroll

Shayok Chakraborty

Ian A Young

Kshitij Arun Doshi

Emily H Qi

Dmitri E Nikonov

Sanjeev N Trika

Jawad Basit Khan

Giuseppe Beppe Raffa

Carl S Marshall

Ignacio Javier Alvarez

Xue Yang

Selvakumar Panneer

Wei Wu

Xingang Guo

Jose Rodrigo Camacho Perez

Paulo Lopez Meyer

Gregory A Peek

Hector A Cordourier Maruri

Julio Cesar Zamora Esquivel

Jeffrey R Foerster

Leobardo E Campos Macias

Ulhas S Warrier

Bradley Alan Jackson

Shawn S Mceuen

Alejandro Ibarra Von Borstel

Anahit Tarkhanyan

Jose K Sia, Jr

Jamie Sherman

Kevin W Bross

Wen-Ling Huang

Mi Sun Park

Anthony G Lamarca

Yevgeniy Y Yarmosh

Joshua Ekandem

Jeffrey Ota

Shilpa A Sodani

Margaret Morris

Kaustubh Das

Michael B Andrews

Hai Li

Punyashloka Debashis

Blanka Vlasak

Anand V Bodas

David Israel González Aguirre

Therese E Dugan

Girish Srinivasa Murthy

Yeongseon Lee

Tanfer Alan

Nafisa A Chowdhury

Cory A Harris

Nilesh Ahuja

Iyer Ravishankar

According to various aspects, controller for an automated machine may include: a processor configured to: compare information about a function of the automated machine with information of a set of tasks available to a plurality of automated machines; negotiate, with the other automated machines of the plurality of automated machines and based on a result of the comparison, which task of the set of tasks is allocated to the automated machine.

Automated machine collaboration

A controller for an automated machine may include including: one or more processors configured to: determine that a group affiliation of the automated machine switched from a first group of automated machines to a second group of automated machines, the first group of automated machines being assigned to one or more first tasks, the second group of automated machines being assigned to one or more second tasks; generate a message for one or more network devices of the second group of automated machines in accordance with a communication protocol, the message including information about a task performing model of the automated machine, the task performing model being based on a result of performing at least one task of the one or more first tasks by the automated machine.

Autonomous machine knowledge transfer

Various aspects are related to a map representation system including one or more processors configured to: obtain operation task data, the operation task data indicating one or more operation tasks associated with an operation of one or more autonomous agents within a corresponding operating space; and obtain operation task specific map data as a function of the operation task data, the operation task specific map data representing the operating space corresponding to the one or more operation tasks.

Map representation system and method for operating an autonomous agent

According to various aspects, a controller for an automated machine may include: one or more processors configured to: obtain a message from a further automated machine in accordance with a communication protocol, the message including a first result of a first sensing process that the further automated machine performs; and determine an assessment of the automated machine based on the first result and based on a second result of a second sensing process that the automated machine performs.

Automated machines and systems

Techniques are disclosed for using neural network architectures to estimate predictive uncertainty measures, which quantify how much trust should be placed in the deep neural network (DNN) results. The techniques include measuring reliable uncertainty scores for a neural network, which are widely used in perception and decision-making tasks in automated driving. The uncertainty measurements are made with respect to both model uncertainty and data uncertainty, and may implement Bayesian neural networks or other types of neural networks.

Robust multimodal sensor fusion for autonomous driving vehicles

Embodiments of the present disclosure are directed toward probabilistic in-memory computing configurations and arrangements, and configurations of probabilistic bit devices (p-bits) for probabilistic in-memory computing. concept with emerging. A probabilistic in-memory computing device includes an array of p-bits, where each p-bit is disposed at or near horizontal and vertical wires. Each p-bit is a time-varying resistor that has a time-varying resistance, which follows a desired probability distribution. The time-varying resistance of each p-bit represents a weight in a weight matrix of a stochastic neural network. During operation, an input voltage is applied to the horizontal wires to control the current through each p-bit. The currents are accumulated in the vertical wires thereby performing respective multiply-and-accumulative (MAC) operations. Other embodiments may be described and/or claimed.

Probabilistic in-memory computing

Example apparatus disclosed herein are to process a first image of a first video segment from the image capture sensor with a machine learning algorithm to determine a first score for the first image, the machine learning algorithm to detect actions associated with images, the actions associated with labels. Disclosed example apparatus are also to determine a second score for the first video segment based on respective first scores for corresponding images in the first video segment. Disclosed example apparatus are further to determine, based on the second score, whether to retain the first video segment in the memory.

Video summarization using semantic information

In one embodiment, an apparatus comprises a memory and a processor. The memory is to store visual data associated with a visual representation captured by one or more sensors. The processor is to: obtain the visual data associated with the visual representation captured by the one or more sensors, wherein the visual data comprises uncompressed visual data or compressed visual data; process the visual data using a convolutional neural network (CNN), wherein the CNN comprises a plurality of layers, wherein the plurality of layers comprises a plurality of filters, and wherein the plurality of filters comprises one or more pixel-domain filters to perform processing associated with uncompressed data and one or more compressed-domain filters to perform processing associated with compressed data; and classify the visual data based on an output of the CNN.

Frequency-domain convolutional neural network

Vehicle navigation control systems in autonomous driving rely on accurate predictions of objects within the vicinity of the vehicle to appropriately control the vehicle safely through its surrounding environment. Accordingly this disclosure provides methods and devices which implement mechanisms for obtaining contextual variables of the vehicle's environment for use in determining the accuracy of predictions of objects within the vehicle's environment.

Devices and methods for accurately identifying objects in a vehicle's environment

In one embodiment, an apparatus comprises processing circuitry to: receive, via a communication interface, a compressed video stream captured by a camera, wherein the compressed video stream comprises: a first compressed frame; and a second compressed frame, wherein the second compressed frame is compressed based at least in part on the first compressed frame, and wherein the second compressed frame comprises a plurality of motion vectors; decompress the first compressed frame into a first decompressed frame; perform pixel-domain object detection to detect an object at a first position in the first decompressed frame; and perform compressed-domain object detection to detect the object at a second position in the second compressed frame, wherein the object is detected at the second position in the second compressed frame based on: the first position of the object in the first decompressed frame; and the plurality of motion vectors from the second compressed frame.

Hybrid pixel-domain and compressed-domain video analytics framework

Multi-domain convolutional neural network

In one embodiment, an apparatus comprises a storage device and a processor. The storage device may store a plurality of compressed images comprising one or more compressed master images and one or more compressed slave images. The processor may: identify an uncompressed image; access context information associated with the uncompressed image and the one or more compressed master images; determine, based on the context information, whether the uncompressed image is associated with a corresponding master image; upon a determination that the uncompressed image is associated with the corresponding master image, compress the uncompressed image into a corresponding compressed image with reference to the corresponding master image; upon a determination that the uncompressed image is not associated with the corresponding master image, compress the uncompressed image into the corresponding compressed image without reference to the one or more compressed master images; and store the corresponding compressed image on the storage device.

Context-aware image compression

In one embodiment, an apparatus comprises a communication interface and a processor. The communication interface is to communicate with a plurality of devices. The processor is to: receive compressed data from a first device, wherein the compressed data is associated with visual data captured by sensor(s); perform a current stage of processing on the compressed data using a current CNN, wherein the current stage of processing corresponds to one of a plurality of processing stages associated with the visual data, and wherein the current CNN corresponds to one of a plurality of CNNs associated with the plurality of processing stages; obtain an output associated with the current stage of processing; determine, based on the output, whether processing associated with the visual data is complete; if the processing is complete, output a result associated with the visual data; if the processing is incomplete, transmit the compressed data to a second device.

Cascade convolutional neural network

Methods, apparatus, systems and articles of manufacture to improve efficiency of object tracking in video frames are disclosed. An example apparatus includes a clusterer to cluster a map of a video frame into blobs; a comparator to determine an intersection over union value between the blobs and bounding boxes in a second video frame; and an interface to initiate object detection by a neural network on the first video frame when the intersection over union does not satisfy a threshold.

Methods and apparatus to improve efficiency of object tracking in video frames

Various systems and methods for implementing distribution of a neural network workload are described herein. A discovery message is encoded that includes a latency requirement and requested resources for a workload of a neural network. A discovery response, from a proximate resource and in response to the discovery message, is decoded and includes available resources of the proximate resource available for the workload based on the requested resources for the workload. The proximate resource is selected to execute the workload based on the available resources of the proximate resource. In response to the discovery response, an offload request is encoded that includes a description of the workload. The description of the workload identifies the node to execute at the proximate resource. In response to the offload request, an input is provided to a ADAS system based on the result.

Proximity-based distributed sensor processing

Growing community of inventors

Portland, OR, United States of America

Omesh Tickoo