Indexing Highly Repetitive String Collections, Part II

2021 ◽  
Vol 54 (2) ◽  
pp. 1-32
Author(s):  
Gonzalo Navarro
Keyword(s):  
Information Content ◽  
Data Structures ◽  
New Technology ◽  
Concrete Structures ◽  
New Combinations ◽  
Moore’S Law ◽  
Moore's Law ◽  
New Generation

Two decades ago, a breakthrough in indexing string collections made it possible to represent them within their compressed space while at the same time offering indexed search functionalities. As this new technology permeated through applications like bioinformatics, the string collections experienced a growth that outperforms Moore’s Law and challenges our ability of handling them even in compressed form. It turns out, fortunately, that many of these rapidly growing string collections are highly repetitive, so that their information content is orders of magnitude lower than their plain size. The statistical compression methods used for classical collections, however, are blind to this repetitiveness, and therefore a new set of techniques has been developed to properly exploit it. The resulting indexes form a new generation of data structures able to handle the huge repetitive string collections that we are facing. In this survey, formed by two parts, we cover the algorithmic developments that have led to these data structures. In this second part, we describe the fundamental algorithmic ideas and data structures that form the base of all the existing indexes, and the various concrete structures that have been proposed, comparing them both in theoretical and practical aspects, and uncovering some new combinations. We conclude with the current challenges in this fascinating field.

Indexing Highly Repetitive String Collections, Part I

2021 ◽  
Vol 54 (2) ◽  
pp. 1-31
Author(s):  
Gonzalo Navarro
Keyword(s):  
Information Content ◽  
Data Structures ◽  
New Technology ◽  
Direct Access ◽  
Basic Aspect ◽  
Moore’S Law ◽  
Algorithmic Techniques ◽  
New Generation ◽  
Compressed Data ◽  
Ideal Measure

Two decades ago, a breakthrough in indexing string collections made it possible to represent them within their compressed space while at the same time offering indexed search functionalities. As this new technology permeated through applications like bioinformatics, the string collections experienced a growth that outperforms Moore’s Law and challenges our ability to handle them even in compressed form. It turns out, fortunately, that many of these rapidly growing string collections are highly repetitive, so that their information content is orders of magnitude lower than their plain size. The statistical compression methods used for classical collections, however, are blind to this repetitiveness, and therefore a new set of techniques has been developed to properly exploit it. The resulting indexes form a new generation of data structures able to handle the huge repetitive string collections that we are facing. In this survey, formed by two parts, we cover the algorithmic developments that have led to these data structures. In this first part, we describe the distinct compression paradigms that have been used to exploit repetitiveness, and the algorithmic techniques that provide direct access to the compressed strings. In the quest for an ideal measure of repetitiveness, we uncover a fascinating web of relations between those measures, as well as the limits up to which the data can be recovered, and up to which direct access to the compressed data can be provided. This is the basic aspect of indexability, which is covered in the second part of this survey.

Fine line panel level fan out changes the SiP landscape

2018 ◽  
Vol 2018 (1) ◽  
pp. 000349-000354
Author(s):  
David Fang ◽  
Michael Hsu ◽  
CC Chang ◽  
KW Chung ◽  
Alex Liu ◽  
...  
Keyword(s):  
Production Efficiency ◽  
Flip Chip ◽  
Lower Cost ◽  
Transmission Loss ◽  
New Technology ◽  
Moore’S Law ◽  
Moore's Law ◽  
Fine Line ◽  
To Come ◽  
Application Requirements

Abstract Moore's Law has been through many challenges in the last few years. The transistors continued to shrink to smaller sizes but the benefit of better performance and lower cost that comes along with shrinking is facing difficulties. Semiconductor industries are trying to come up with new ways to keep the Moore's Law going on two different fronts: where foundries are working on more Moore solutions and packaging houses are working on more than Moore solutions. Recently the industry has been considering the chip splitting and re-constitution in the form of SiP which has relatively shorter development time and lower cost than the SoC. But traditional SiP with wirebonding or FC connections to substrate will lead to high transmission loss and power consumption. A new fine line SiP solution is required to shorten the connection between chips to improve the performance. Different from the 3DIC and 2.5DIC technologies, fine line panel level fan out has the advantages of good performance, design flexibility, and high production efficiency. This paper will discuss about the challenges in setting up this technology including establishing standards, tools preparation, and process difficulties. The dedicated machines that handle the fine line panel level fan out are critical. It is not easy to select suitable tools for this new technology. We also need to co-develop with tool vendors for some process stages which suitable tools from existing industries could not be easily found. Additionally, panel warpage and chip shift are two of major process challenges. Experiences on overcoming these difficulties will be shared. Different structures and processes have been developed for varied application requirements. The chip first approach encapsulates chips and then build RDL layers on the encapsulation surface. It is suitable for mobile AP, baseband, ASIC, PMIC, and memory. The chip last solution build RDL first, then flip chip mounting the bumped chips on the RDL. The RDL can be tested before the mounting of chips. It is suitable for CPU, GPU, FPGA, and thermal sensitive devices. Pillars in fan out is a chip middle solution. It uses Cu pillars to connect top and bottom RDLs which is good for chip stacking. Currently the 5/5um line/space is already been qualified. 3/3um under development and tool capability is 2/2um. Several real cases will be demonstrated in this paper to help the readers understand this technology. This technology is expected to be crucial for the coming era of 5G, automotive, IoT, and AI. It is believed that this technology can be applied to different kinds of end applications. For example, multi-chip stacking in a fan out package to achieve high bandwidth performance. Fan out stacking of logic and memory chips which can replace the existing PoP. Using fan out to integrate passives and/or other chips can achieve a compact SiP. Fan out could be one of the embedded substrate. Fan out RDL process can also be a suitable platform for antenna in package designs. This paper will introduce the challenges of Moore's law as beginning, and then explain the advantages and the challenges of fine line panel fan out technology, and the proposed approaches to address those challenges.

How Moore's Law is Enabling a New Generation of Telecommunications Payloads

2014 ◽  
Author(s):  
Stephen P. Brown ◽  
Chiok K. Leong ◽  
Paul S. Cornfield ◽  
Andrew M. Bishop ◽  
Robert J. Hughes ◽  
...  
Keyword(s):  
Moore’S Law ◽  
Moore's Law ◽  
New Generation

Chips with Everything

2017 ◽  
Author(s):  
David Segal
Keyword(s):  
Quantum Computing ◽  
Integrated Circuits ◽  
Critical Role ◽  
Moore’S Law ◽  
Moore's Law ◽  
Silicon Chips ◽  
History Of ◽  
Future Potential ◽  
Digital Computers ◽  
Dna Computer

Chapter 3 highlights the critical role materials have in the development of digital computers. It traces developments from the cat’s whisker to valves through to relays and transistors. Accounts are given for transistors and the manufacture of integrated circuits (silicon chips) by use of photolithography. Future potential computing techniques, namely quantum computing and the DNA computer, are covered. The history of computability and Moore’s Law are discussed.

From Moore's Law to the Carbon Law

2020 ◽  
Author(s):  
Daniel Pargman ◽  
Aksel Biørn-Hansen ◽  
Elina Eriksson ◽  
Jarmo Laaksolahti ◽  
Markus Robèrt
Keyword(s):  
Moore’S Law ◽  
Moore's Law

scholarly journals A study on the numerical modelling of UHPFRC-strengthened members

2018 ◽  
Vol 199 ◽  
pp. 09001
Author(s):  
Renaud Franssen ◽  
Serhan Guner ◽  
Luc Courard ◽  
Boyan Mihaylov
Keyword(s):  
Numerical Modelling ◽  
High Performance ◽  
Concrete Structures ◽  
Concrete Members ◽  
High Durability ◽  
Tension Tests ◽  
The World ◽  
Aging Infrastructure ◽  
New Generation ◽  
Modelling Approach

The maintenance of large aging infrastructure across the world creates serious technical, environmental, and economic challenges. Ultra-high performance fibre-reinforced concretes (UHPFRC) are a new generation of materials with outstanding mechanical properties as well as very high durability due to their extremely low permeability. These properties open new horizons for the sustainable rehabilitation of aging concrete structures. Since UHPFRC is a young and evolving material, codes are still either lacking or incomplete, with recent design provisions proposed in France, Switzerland, Japan, and Australia. However, engineers and public agencies around the world need resources to study, model, and rehabilitate structures using UHPFRC. As an effort to contribute to the efficient use of this promising material, this paper presents a new numerical modelling approach for UHPFRC-strengthened concrete members. The approach is based on the Diverse Embedment Model within the global framework of the Disturbed Stress Field Model, a smeared rotating-crack formulation for 2D modelling of reinforced concrete structures. This study presents an adapted version of the DEM in order to capture the behaviour of UHPFRC by using a small number of input parameters. The model is validated with tension tests from the literature and is then used to model UHPFRC-strengthened elements. The paper will discuss the formulation of the model and will provide validation studies with various tests of beams, columns and walls from the literature. These studies will demonstrate the effectiveness of the proposed modelling approach.

scholarly journals QUBO formulations for training machine learning models

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Prasanna Date ◽  
Davis Arthur ◽  
Lauren Pusey-Nazzaro
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Large Scale ◽  
Support Vector ◽  
Quantum Computers ◽  
Np Hard ◽  
Learning Models ◽  
Moore’S Law ◽  
Moore's Law ◽  
Machine Learning Models

AbstractTraining machine learning models on classical computers is usually a time and compute intensive process. With Moore’s law nearing its inevitable end and an ever-increasing demand for large-scale data analysis using machine learning, we must leverage non-conventional computing paradigms like quantum computing to train machine learning models efficiently. Adiabatic quantum computers can approximately solve NP-hard problems, such as the quadratic unconstrained binary optimization (QUBO), faster than classical computers. Since many machine learning problems are also NP-hard, we believe adiabatic quantum computers might be instrumental in training machine learning models efficiently in the post Moore’s law era. In order to solve problems on adiabatic quantum computers, they must be formulated as QUBO problems, which is very challenging. In this paper, we formulate the training problems of three machine learning models—linear regression, support vector machine (SVM) and balanced k-means clustering—as QUBO problems, making them conducive to be trained on adiabatic quantum computers. We also analyze the computational complexities of our formulations and compare them to corresponding state-of-the-art classical approaches. We show that the time and space complexities of our formulations are better (in case of SVM and balanced k-means clustering) or equivalent (in case of linear regression) to their classical counterparts.

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