Browsing by Author "Palem, Krishna"
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Item Addressable SiOX memory array with incorporated diodes(2016-07-05) Tour, James M.; Yao, Jun; Lin, Jian; Wang, Gunuk; Palem, Krishna; Rice University; Nanyang Technological University; United States Patent and Trademark OfficeVarious 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 SiOx, SiOxH, SiOxNy, SiOxNyH, SiOxCz, SiOxCzH, 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.Item Camera-based positioning system using learning(2021-05-04) Shrivastava, Anshumali; Luo, Chen; Palem, Krishna; Moon, Yongshik; Noh, Soonhyun; Park, Daedong; Hong, Seongsoo; Rice University; Seoul National University R&DB Foundation; United States Patent and Trademark OfficeA 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.Item Characterizing Algorithmic Efficiencies through Concentration(2021-08-13) Pham, Hung; Palem, KrishnaUnderstanding inherent bottlenecks to efficient algorithm design lies at the heart of computer science. This question is significant both in the classical computing domain as well as in the emerging context of quantum computing. In this thesis, my goal is to characterize bottlenecks in designing efficient algorithms through the lens of a parameter called concentration of functions, starting with the domain of quantum information. My primary focus is probabilistically approximately correct (PAC) learning. I chose this domain since it allows us to approach the important subject of supervised learning in a rigorous and principled manner. For PAC learning, I propose a quantum algorithm to learn the class of concentrated Boolean functions with complexity $ O(\frac{M}{\epsilon^2})$ which offers an advantage over the best known classical PAC algorithms with complexity $\tilde{O}(n^2M)$, where M denotes the number of the concentration terms. I also show a lower bound $\Omega(M)$ to PAC learn this class of functions in distribution-independent settings. All this work is done in the context of the standard query model for PAC learning where the complexity measure is the number of queries, dubbed query complexity. I extend this work to include the learning model where functions are learned without any error, which is often called exact learning, and prove a query complexity lower bound of $\Omega(\frac{\epsilon \log M}{n} 2^n)$ in exact learning the class of concentrated Boolean functions. In the next part of the thesis, I focus my work on classical algorithms and explore a combinatorial counterpart of concentration called degree of symmetry. In this arena, graph isomorphism is my problem of choice. Once again, my goal is to characterize the efficiency of algorithms, in particular parallel algorithms, for graph isomorphism based on a concentration-related parameter. In particular, I propose a parallel algorithm in polynomial time using a quasi-polynomial number of processors for the Graph Isomorphism problem. My work builds on Babai’s celebrated quasi-polynomial algorithm and is work-preserving. The idea behind the parallelization explores the symmetry of the input structure for easier parallelization.Item Innovation for Sustainability in Information and Communication TechnologiesBronk, Christopher; Lingamneni, Avinash; Palem, Krishna; James A. Baker III Institute for Public PolicyItem Location Estimation Through Inexact Machine Learning Approach(2018-10-25) Gonzalez Espana, Juan Jose; Palem, KrishnaLocation estimation has become a field of increasing interest in recent years. The main reason is the multiple applications that can be enabled based on this technology. Fields such as entertainment, health care, tourism and advertisement are some of the areas where a plethora of applications can be implemented. In outdoors this problem is solved, for most of the cases, with Global Navigation Systems (GNSS). However, in indoors is a current topic of interest that has been addressed from different perspectives with different technologies. Nonetheless, there is no technology that is as established as GNSS is for outdoors. One promising approach is Inertial Measurement Units (IMU) which are low cost and widely accessible in multiple SmartDevices such SmartPhones, SmartWatches, WristBands, among others. Two of the main difficulties that hinder the wide adoption of this technology are the error accumulation between estimations and the scarce availability of the Ground Truth data to train and test the models. In this work both challenges are addressed by two methods, one which corrects the error by using the structure of the map where the user is located and the other method improves the Ground Truth data provided by GNSS measurements. Energy consumption is reduced by a factor 27x when compared with GPS and the accuracy of the labels is improved by 26% on average.Item The Platform-Aware Compilation Environment: Status and Future Directions(2012-06-13) Cooper, Keith D.; Khan, Rishi; Lele, Sanjiva; Mellor-Crummey, John; Merényi, Erzsébet; Palem, Krishna; Sadayappan, P.; Sarkar, Vivek; Tatge, Reid; Torczon, LindaThe Platform-Aware Compilation Environment (PACE) is an ambitious attempt to construct a portable compiler that produces code capable of achieving high levels of performance on new architectures. The key strategies in PACE are the design and development of an optimizer and runtime system that are parameterized by system characteristics, the automatic measurement of those characteristics, the extensive use of measured performance data to help drive optimization, and the use of machine learning to improve the long-term effectiveness of the compiler and runtime system.