![]() From an implementation point of view, the design of portable IoT devices for user applications in embedded systems with limited resources imposes severe requirements in terms of computational capacity, memory, and power consumption, which poses an open research challenge for the electronic engineering community. Modern IoT devices incorporate a wide range of sensors to capture information about their surroundings, as well as a set of complex algorithms to process that information. The rapid growth of the Internet of Things (IoT) has required a concentration of efforts in the development and deployment of efficient operational architectures to support and provide a multiplicity of new applications and services. It also includes details of their plug and play inclusion as hardware accelerators in the C implementation of this public-key encryption scheme codified in the LibNTRU library, showing that acceleration factors of up to 3.1 are achieved when compared to pure software implementations running on the processing systems included in the programmable devices. The work provides an extensive set of implementation and characterization results in devices of the Xilinx Zynq-7000 and Zynq UltraScale+ families for the different sets of parameters defined in the NTRUEncrypt standard. ![]() The designs are provided as AXI4 bus-compliant intellectual property modules that can be easily incorporated into embedded systems developed with the Vivado design tools. ![]() ![]() The flexibility in selecting the design parameters and the interconnection protocol with a general-purpose processor allow them to be applied both to the standardized variants of NTRU and to the new proposals that are being considered in the post-quantum contest currently held by the National Institute of Standards and Technology, as well as to obtain an adequate cost/performance/security-level trade-off for a target application. This article describes the hardware implementation of parameterized multi-unit serial polynomial multipliers to speed up time-consuming operations in NTRU-based cryptographic schemes. Modern lattice-based cryptographic schemes have proven to be a good alternative, both to face the security threats that arise as a consequence of the development of quantum computing and to allow efficient implementations of cryptographic primitives in resource-limited embedded systems, such as those used in consumer and industrial applications of the IoT. We also provide our visions on the future trend of AI chip designs.Concern for the security of embedded systems that implement IoT devices has become a crucial issue, as these devices today support an increasing number of applications and services that store and exchange information whose integrity, privacy, and authenticity must be adequately guaranteed. We discuss various architectures that support DNN executions in terms of computing units, dataflow optimization, targeted network topologies, architectures on emerging technologies, and accelerators for emerging applications. In this article, we focus on summarizing the recent advances in accelerator designs for deep neural networks (DNNs)-that is, DNN accelerators. These platforms, which belong to a larger category named “domain-specific computing,” focus on specific customization for AI. Computing platforms that are dedicatedly designed for AI applications have been considered, ranging from a complement to von Neumann platforms to a “must-have” and stand-alone technical solution. Although the explosion of big data applications is driving the development of ML, it also imposes severe challenges of data processing speed and scalability on conventional computer systems. Machine learning (ML) approaches have been successfully applied to solve many problems in academia and in industry. Recently, due to the availability of big data and the rapid growth of computing power, artificial intelligence (AI) has regained tremendous attention and investment.
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Update : Above link deprecated, try the following: I went off to my usual search Website and after some research I found a page that documented a reader of sorts. I asked myself a question? Has anyone done this before? The Arduino in turn would be responsible of interfacing with the EPROM and fetching the required data. A different read command was sent depending on the EPROM size selected by the user. On the PC end it consisted of a Windows application that would send a “read” command to the Arduino. I then put together a block diagram of the reader. Serial communications was easily handled by a slew of languages. I liked this option because it already included the USB interface which could be used as though it were a serial interface. My preferred communications method is serial through USB since no modern computers have a serial port (RS232) anymore.Īn option I considered was using a USB/Serial adapter who would then connect to the necessary electronics that would put the address out on the corresponding lines and would then fetch the data that the EPROM would spit out. The second task was determining how I was going to get the data from the EPROM back to the computer. The first task was determining how I was going to get the address to the EPROM. ![]() This project interested me because of its learning potential. I left that project on the shelf, deciding to first look further into the reader. Other thoughts included an EPROM emulator where some modern storage media could be used to emulate the physical functionality of an EPROM. I wondered what was needed to build just a reader. ![]() ![]() I found that most of the items that I found were readers/burners. I was curious enough about these long forgotten components (so forgotten that when I search for “EPROM” on Google it thinks it is a misspelling and corrects it to “EEPROM”) that I started to look for a reader. I recall having to take numerous factors into consideration so that the code would run correctly. I recall that way back in the 80s I coded a small program that I put on an EPROM and ran when the computer was started. It all started at the beginning of 2015 when an EPROM related topic was discussed in the Color Computer mailing list where I participate. Blank lines indicate the end of a paragraph. ![]() This example uses just a few formatting commands. The above Markdown produces the following in the knitted document. The following Markdown source text is one way to write this example. Let's say we wish to create a document with an introductory paragraph followed by a sub header, another paragraph, and finally a list. We will look at a simple example as an introduction to the Markdown syntax. This makes Markdown documents easy to type and makes it easy to read the unformatted document. Markdown uses #, *, blank lines, and indented lines for the most common formatting commands. For more information on the metadata section see html metadata section and pdf metadata section. This article series will not change or edit the metadata section. The RmdExamples file, which is opened in the source pane, has the following metadata section. Markdown commands and text will be added after the metadata section. This set of lines between and including the lines with -, is called the metadata section. R Markdown files start with a set of lines which begin with - on a line and end similarly with a line containing. We will write our Markdown files using RStudio's editor. Markdown files can be written using any plain text editor such as notepad. As you gain experience with R Markdown, you will knit much less frequently. As you start working with R Markdown, you may find it convenient to knit your document often. The examples and exercises in this article series are designed to give you the practice and experience needed to be comfortable with this type of document creation process. Writing R Markdown documents is a little different process than with "What You See Is What You Get" (WYSIWYG) editors, such as Microsoft Word. In this article series we will generate HTML files. pdf files are widely available at no cost. We recommend knitting to either pdf or HTML files. We do not recommend knitting to Word, because a Word document is a form that is tempting to edit. Any edits that are made by hand will be lost when the document is knit again. The knitted documents should not be changed by hand. R Markdown files can be knit to html, pdf, or Word documents. These steps are done together for you by simply pushing the knit button in RStudio. The second step uses the Markdown formatting commands to format the final document. The results of the R commands are incorporated with the text and Markdown commands from the. In the first step, the R commands are run. RStudio creates a document, this is called knitting, from an. This results in not only greater efficiency, but also fewer errors in documents. There is no more going back through documents trying to find every thing that needs to be fixed when an analysis is rerun. ![]() Any changes that occur in either your data set or the analysis are automatically updated in your document the next time the document is created. Incorporating R results directly into your documents is an important step in reproducible research. This allow you write documents which integrate results from your analysis. R Markdown adds a few features which include R code and results in the formatted document. Markdown is a good choice to format most documents. The source files are also easier to read than LaTeX or HTML. This small set of features supports the most commonly used formatting. Markdown's formatting commands are simpler than most other formatting languages, such as LaTeX or HTML, because it has a smaller number of features. md, contain text and formatting commands. Markdown is a tool used to create formatted documents. This can be very helpful in determining why results have changed. Git diff allows you to look at what has changed in a file, or files, between any two saved project states. These features make R Markdown documents easy to write and the process less error prone. This set of features supports the most commonly used formatting, resulting in the ability to create most documents. This ease is a result of R Markdown only using a small set of features and this reduces the complexity of the needed commands. The second advantage is it is incredibly easy to use. The first is it allows the results of R code to be directly inserted into formatted documents. R Markdown has two advantages that are of interest to a researcher. These tools are part of RStudio's development environment. This article will introduce you to R Markdown, a document writing program, and demonstrates using RStudio's Git diff, a tool to examine when prior changes were made to a project. ![]() Spanish speaker wanting to learn Japanese? This isn’t such a selling point for native English speakers again, if I head to something like Duolingo as a speaker of English, I’m very lucky: I can learn around 30 languages.ĭuolingo has nothing but a 40% completed English course. There’s also the benefit of flexibility that comes with uTalk, because you don’t need a source language. Languages are incredibly diverse in their grammar, syntax, vocab, idioms and more, so even basic tailoring to so many languages wouldn’t be doable.įrankly, you’re not going to be able to get anywhere near fluent with uTalk alone, but that doesn’t mean it can’t be a valuable part of your overall language-learning journey as long as you recognise these inherent limitations.Įspecially considering it’s a tiny fraction of the price of Rosetta Stone, I feel like it’s value for money. There are some inherent limitations in trying to teach 140 languages on one platform with the same themes and vocab The language offerings aren’t tailored for the rules of the languages in any way, unlike Rocket Languages for example which really teaches how the pieces of that particular language fit together.īut that doesn’t mean that what uTalk does isn’t valuable in and of itself. If you’re expecting fluency from one app, this is definitely not the app. What I hadn’t really understood at the time was that, as uTalk’s product manager Simon explains:įor those seeking fluency, one app is never going to be enough (ours included!) I had access to Rosetta Stone in primary school, and all I’d learned from it was to internalise the idea that I probably wasn’t any good at language learning. uTalk’s plethora of language combinations In other words, you’re learning to speak in the same way that a first-language speaker does, without needing to involve your native language at all. It uses the same basic system for all the languages, so you don’t even need to know English you could be a French speaker learning Mandarin, or a Bhutanese learning Greek. There’s over a hundred languages on the app, from lesser-known languages like Tibetan and Yoruba to the most popular languages like Spanish, French, Mandarin and Japanese. This gets you speaking faster and improves your listening skills. ![]() Not just how it’s written (unlike many language-learning methods) but also how it sounds. You can then use the uCoins to unlock languages and features. There’s also an in-app currency call uCoins which you can earn by completing lessons (or purchasing them). UTalk is an app for Android, iOS (includes “offline mode”) and web that lets you “learn any language” through visuals you see the pictures, you listen to what they are, and you play a variety of games designed to help you commit the meaning of your new vocabulary to memory. |
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