William Marsh Rice University

Seoul National University

Nanyang Technological University

Krishna Palem

James Mitchell Tour

Daedong Park

Jun Yao

Jian Lin

Seongsoo Hong

Gunuk Wang

Anshumali Shrivastava

Chen Luo

Soonhyun Noh

Lakshmi Narasimhan Chakrapani

Avinash Lingamneni

Yongshik Moon

A device, system, and methods are described to perform machine-learning camera-based indoor mobile positioning. The indoor mobile positioning may utilize inexact computing, wherein a small decrease in accuracy is used to obtain significant computational efficiency. Hence, the positioning may be performed using a smaller memory overhead at a faster rate and with lower energy cost than previous implementations. The positioning may not involve any communication (or data transfer) with any other device or the cloud, providing privacy and security to the device. A hashing-based image matching algorithm may be used which is cheaper, both in energy and computation cost, over existing state-of-the-art matching techniques. This significant reduction allows end-to-end computation to be performed locally on the mobile device. The ability to run the complete algorithm on the mobile device may eliminate the need for the cloud, resulting in a privacy-preserving localization algorithm by design since network communication with other devices may not be required.

Camera-based positioning system using learning

Various embodiments of the resistive memory cells and arrays discussed herein comprise: (1) a first electrode; (2) a second electrode; (3) resistive memory material; and (4) a diode. The resistive memory material is selected from the group consisting of SiO, SiOH, SiON, SiONH, SiOC, SiOCH, and combinations thereof, wherein each of x, y and z are equal to or greater than 1 and equal to or less than 2. The diode may be any suitable diode, such as n-p diodes, p-n diodes, and Schottky diodes.

Addressable SiOX memory array with incorporated diodes

In general, in one aspect, the invention relates to a computer readable medium including software instructions which, when executed by a processor, perform a method, the method including receiving a first method call from an application, wherein the first method call is associated with a first application component; obtaining a first application component error tolerance (ACET) value associated with the first method call; determining, using the first ACET value and a first inexact amount value (IAV) of a first inexact computing architecture (ICA) processor, that the first ICA processor is available to execute the first method call; and processing the first method call using the first ICA processor.

Computing device using inexact computing architecture processor

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Houston, TX, United States of America

Krishna Palem