Embedded

2022 - 9 - 20

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Image courtesy of "Analytics Insight"

Embedded Analytics: It's Not as Difficult as You Think (Analytics Insight)

As a part of business intelligence, embedded analytics is an advance analytics feature that integrates analytical capabilities and data visualisation into ...

For example, we can use it to create our own analytics project to show us the most important information about our customers’ preferences for our products. You can easily create an application using the data provided by Yellowfin and display it on your website. For example, we can use it to display a chart showing the total number of new customers created in the last month. Using the embedded analytics service, we don’t need to worry about setting up the application or maintaining it on our own. You can personalize your dashboard to your business and choose what needs to be included. In that case, we can use the chart or map provided by Yellowfin to understand the wealth disparity in that city. For example, suppose we have an application that analyzes the digital divide for a particular city. Using embedded analytics, we can create an application that works in real-time and makes decisions based on this information. It’s different from the traditional external analytics (such as Google Analytics), that we have to access outside our applications. This article will answer these questions and give you a coherent idea of embedded analytics and how we can use its features. Embedding real-time reports and dashboards allows end-users to analyze the data held within the software applications into which the analytics platform is embedded. Everyone wants to get their hands on this new technology and use it to build a “smart” solution that can interact with the real world of everyday life.

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Image courtesy of "Electronic Design"

How Embedded Memory Will Drive an Emerging Technology to ... (Electronic Design)

It seems a new memory technology is needed to shrink MCUs and SoCs without redesigning the memory system and keep costs in check.

What we know is that there’s room for only one of these to succeed in a big way, and the others will continue to serve market niches. For discrete memory chips, this fact presents a nearly impenetrable barrier that stands in the way of widespread adoption. Therefore, high-volume production of SoC and MCU wafers that include MRAM, for example, would drive down the production cost of discrete MRAM at the same time. With this understanding, we have been able to compile a 10-year forecast for memory revenues that projects the annual revenues for both embedded and standalone emerging memories shown in Figure 2, which was taken from the report. The second of these is a key reason for Intel’s recent announcement that Optane was to be “wound down.” Note that the MRAM line represents combined discrete and embedded memory. The data in this chart, which runs from 90 nm to 5 nm, shows that the area of SRAM cells in research chips has shrunk an average of 17% per process node while the process has shrunk an average of 21%. In fact, there is, and it comes in the form of emerging memories—those memories that aren’t mainstream today, but could allow the industry to continue to reduce chip costs through process shrinks (black line in Fig. Several others are in development in the hope of making their mark on the industry. These emerging memories are the subject of a new report from Objective Analysis and Coughlin Associates: What has that to do with discrete memory chips, and who says that memory can’t be supported on CMOS logic? While that can get expensive, since SRAM used six transistors per bit and NOR uses only one, it solves the current problem, and serial NOR chips are pretty cheap.

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