The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

[서평]「Applied Cryptography」

Currently, the network traffic control systems are mainly composed of the Internet core and wired/wireless heterogeneous backbone networks. Recently, these packet-switched systems are experiencing an explosive network traffic growth due to the rapid development of communication technologies. The existing network policies are not sophisticated enough to cope with the continually varying network conditions arising from the tremendous traffic growth. Deep learning, with the recent breakthrough in the machine learning/intelligence area, appears to be a viable approach for the network operators to configure and manage their networks in a more intelligent and autonomous fashion. While deep learning has received a significant research attention in a number of other domains such as computer vision, speech recognition, robotics, and so forth, its applications in network traffic control systems are relatively recent and garnered rather little attention. In this paper, we address this point and indicate the necessity of surveying the scattered works on deep learning applications for various network traffic control aspects. In this vein, we provide an overview of the state-of-the-art deep learning architectures and algorithms relevant to the network traffic control systems. Also, we discuss the deep learning enablers for network systems. In addition, we discuss, in detail, a new use case, i.e., deep learning based intelligent routing. We demonstrate the effectiveness of the deep learning-based routing approach in contrast with the conventional routing strategy. Furthermore, we discuss a number of open research issues, which researchers may find useful in the future.

State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems

In this paper, we present a new strategy for pricing average value options, i.e. options whose payoff depends on the average price of the underlying asset over a fixed period leading up to the maturity date. Such options are of particular interest and importance for thinly-traded assets (e.g. crude oil), since price manipulation is inhibited, and both the investor and issuer enjoy a welcome degree of protection from the vagaries of the market. These options are often implicit in a bond contract, although they also appear in a straightforward form. Our results suggest that the price of an average-value option will always be lower than that of a standard European option. Our pricing strategy involves Monte Carlo simulation with variance reduction elements and offers an enhanced pricing method to both arbitragers and hedgers, as well as to the issuers of such bonds.

A Pricing Method for Options Based on Average Asset Values

We present a new approach for integrating virtualized FPGA-based hardware accelerators into commercial-scale cloud computing systems, with minimal virtualization overhead. Partially reconfigurable regions across multiple FPGAs are offered as generic cloud resources through OpenStack (open-source cloud software), thereby allowing users to "boot" custom designed or predefined network-connected hardware accelerators with the same commands they would use to boot a regular Virtual Machine. We propose a hardware and software framework to enable this virtualization. This is a first attempt at closely fitting FPGAs into existing cloud computing models, where resources are virtualized, flexible, and have the illusion of infinite scalability. Our system can set up and tear down virtual accelerators in approximately 2.6 seconds on average, much faster than regular virtual machines. The static virtualization hardware on the physical FPGAs causes only a three cycle latency increase and a one cycle pipeline stall per packet in accelerators when compared to a non-virtualized system. We present a case study analyzing the design and performance of an application-level load balancer using a fully implemented prototype of our system. Our study shows that FPGA cloud compute resources can easily outperform virtual machines, while the system's virtualization and abstraction significantly reduces design iteration time and design complexity.

FPGAs in the Cloud: Booting Virtualized Hardware Accelerators with OpenStack

Run-time Partial Reconfiguration (PR) speed is significant in applications especially when fast IP core switching is required. In this paper, we propose to use Direct Memory Access (DMA), Master (MST) burst, and a dedicated Block RAM (BRAM) cache respectively to reduce the reconfiguration time. Based on the Xilinx PR technology and the Internal Configuration Access Port (ICAP) primitive in the FPGA fabric, we discuss multiple design architectures and thoroughly investigate their performance with measurements for different partial bitstream sizes. Compared to the reference OPB HWICAP and XPS HWICAP designs, experimental results showthatDMA HWICAP and MST HWICAP reduce the reconfiguration time by one order of magnitude, with little resource consumption overhead. The BRAM HWICAP design can even approach the reconfiguration speed limit of the ICAP primitive at the cost of large Block RAM utilization.

/pdf/run-time-partial-reconfiguration-speed-investigation-and-73o68hhpo5.pdf

Run-time Partial Reconfiguration speed investigation and architectural design space exploration

Runtime Reconfiguration (RTR) has been traditionally utilized as a means for exploiting the flexibility of High-Performance Reconfigurable Computers (HPRCs). However, the RTR feature comes with the cost of high configuration overhead which might negatively impact the overall performance. Currently, modern FPGAs have more advanced mechanisms for reducing the configuration overheads, particularly Partial Runtime Reconfiguration (PRTR). It has been perceived that PRTR on HPRC systems can be the trend for improving the performance. In this work, we will investigate the potential of PRTR on HPRC by formally analyzing the execution model and experimentally verifying our analytical findings by enabling PRTR for the first time, to the best of our knowledge, on one of the current HPRC systems, Cray XD1. Our approach is general and can be applied to any of the available HPRC systems. The paper will conclude with recommendations and conditions, based on our conceptual and experimental work, for the optimal utilization of PRTR as well as possible future usage in HPRC.

Exploiting Partial Runtime Reconfiguration for High-Performance Reconfigurable Computing

High-performance reconfigurable computers (HPRCs) are parallel computers but with added FPGA chips. Examples of such systems are the Cray XT5h and Cray XD1, the SRC-7 and SRC-6, and the SGI Altix/RASC. The execution of parallel applications on HPRCs mainly follows the single-program multiple-data (SPMD) model, which is largely the case in traditional high-performance computers (HPCs). In addition, the prevailing usage of FPGAs in such systems has been as co-processors. The overall system resources, however, are often underutilized because of the asymmetric distribution of the reconfigurable processors relative to the conventional processors. This asymmetry is often a challenge for using the SPMD programming model on these systems. In this work, we propose a resource virtualization solution based on partial run-time reconfiguration (PRTR). This technique will allow sharing the reconfigurable processors among the underutilized processors. We will present our virtualization infrastructure augmented with an analytical investigation. We will verify our proposed concepts with experimental implementations using the Cray XD1 as a testbed. It will be shown that this approach is quite promising and will allow full exploitation of the system resources with fair sharing of the reconfigurable processors among the microprocessors. Our approach is general and can be applied to any of the available HPRC systems.

/pdf/virtualizing-and-sharing-reconfigurable-resources-in-high-4jieqzs92s.pdf

Virtualizing and sharing reconfigurable resources in High-Performance Reconfigurable Computing systems

The authors present different alternatives to increase the performance of ciphering algorithms in embedded systems based on MicroBlaze, a soft-core processor especially designed for Xilinx field programmable gate arrays. Several implementations of the most usual cryptographic algorithms have been developed for comparison purposes. The architectural options offered by MicroBlaze, together with the different choices to connect custom-accelerating cores, are considered. Without a great design effort, an improvement reaching two orders of magnitude over all-software solutions could be obtained.

Ciphering algorithms in MicroBlaze-based embedded systems

The complexity exhibited by pervasive systems is constantly increasing. Customer electronics devices provide day to day a larger amount of functionalities. A common approach for guaranteeing high performance is to include specialized coprocessor units. However, these systems lack flexibility, since one must define, in advance, the coprocessor functionality. A solution to this problem is to use run-time reconfigurable coprocessors, exploiting the advantages of hardware while keeping a flexible platform. In this paper, we describe a self-reconfigurable pervasive platform containing a dynamically reconfigurable cryptographic coprocessor. As case-study, we consider three ciphering algorithms and we compare the performance of the coprocessor against a full-software implementation. The number of ciphering algorithms can be infinitely extended using a remote server.

Self-Reconfigurable Pervasive Platform for Cryptographic Application

Numerical non-robustness is a recurring phenomenon in scientific computing. It is primarily caused by numerical errors arising because of fixed-precision arithmetic in integer and/or floating-point computations. Exact computation, based on arbitrary-precision arithmetic, has been developed over the last decade as an emerging numerical computation paradigm in response to this problem of numerical non-robustness. Exact arithmetic, specifically arbitrary-precision arithmetic, has been traditionally implemented using efficient software libraries such as GNU multi-precision (GMP). However, this results in a slower arithmetic performance when compared to fixed-precision arithmetic. In this paper we present a first effort, to the best of our knowledge, of reconfigurable hardware support for arbitrary-precision arithmetic. The proposed hardware architectures are based on virtual convolution scheduling which is derived from a formal representation of the problem. Targeting high performance and efficiency, dynamic (non-linear) pipelines techniques were exploited to eliminate the effects of deeply-pipelined operators. Referenced to GMP, our experiments showed promising results.

Bringing High-Performance Reconfigurable Computing to Exact Computations

Popular Tasks used by Researchers

How can I help with your research?

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

We encountered an error while loading the panel. Please try refreshing the page.

Something went wrong

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

Anyone with the link can view this chat. You can switch it back to private anytime.

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Ivan Gonzalez

Author Tools

Chat about Author