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5204-0020-75_Datasheet PDF

时间:2021-06-14 01:30:24 来源:网络整理编辑:Mill-Max

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FPGAs have some advantages in cryptographic applications. They are highly flexible, allowing the reprogramming of algorithms to adapt to brute force attacks by hackers. That flexibility can also be the FPGA's Achilles heel if it does not implement countermeasures, however.

FPGAs have some advantages in cryptographic applications. They are highly flexible, allowing the reprogramming of algorithms to adapt to brute force attacks by hackers. That flexibility can also be the FPGA's Achilles heel if it does not implement countermeasures, however.

Setting up a practical flow It is probably no big surprise that this needs to be a push-button batch flow. You don’t want to have to do anything interactive unless a problem is flagged. If you have a lot of IP (or internal blocks) being upgraded asynchronously, you may want to consider using a make command to further optimize inspection runs. Set up correctly, make can also help you manage differing directory/file organizations for IP from different sources, ensure the correct IP release is being checked, and more.

Here’s a list of quick, but fairly comprehensive checks you should include under make or a shell script. First the objective checks:

5204-0020-75_Datasheet PDF

Even on a large sub-system, these checks can easily be completed within a few hours.

Now the regression checks. It is important to remember in regression comparisons that I do not necessarily want to compare the latest release with the immediately prior release. I may have last checked out an IP at version 1.3 and now want to upgrade to version 1.5, and the latest version is 1.7. A usable regression script must allow me to specify which versions of which IPs I am currently using and to which versions I want to upgrade. It should be possible to automate building the current list, using native scripting in your source code management system.

An easy way to setup these comparisons is to gather all of the above data in a datasheet format, then compare datasheets. Of course you want to run the comparison in such a way that only differences are highlighted, but that should not be particularly challenging for a good scripter. For many of these comparisons, you also want to give some thought to what constitutes a significant difference. For I/O pins, any change is significant. For domain crossings, an increase in the number of unsynchronized crossings would be significant. For area and logic path depths, an increase by perhaps more than 15% would be significant. For test coverage and timing constraint coverage, a decrease would be significant. Figure 2 is an example of a machine-generated datasheet.

5204-0020-75_Datasheet PDF

Setup correctly, both objective and regression analysis together, even on a complex processor-scale IP, should take no more than a few hours. Under make, checks need only be performed on IP which has been updated. As a result, incoming inspection checks for all the IP on which a given design depends can be reduced to an overnight run – significantly more practical than a week-long analysis based on production tools.

A real example Theory is great, but what kind of results does this strategy deliver in practice? One large semiconductor company that I know provides a good example. They build complex SoCs, including smartphones and multimedia devices. They use IP from a rich variety of sources – from the standard external suppliers (ARM, Synopsys, Imagination and others), from an internal central IP group (who supply bus fabric, interrupt and security IP) and from other divisions (who internally share successful differentiated subsystems, including video and audio processors, among other IP).

5204-0020-75_Datasheet PDF

Given such a complex IP environment, this company recognized many years ago that a disciplined process for incoming IP inspection was necessary and already instituted a methodology based on a flow using production tools. This worked well, but as the complexity of the IP and the size of the library increased, they found, even with make, a typical library validation run would take one work week. This became a major problem for design schedules given that IPs were being upgraded frequently.

Recently, this company instituted a process similar to the one I have described. Using Atrenta’s SpyGlass RTL analysis product, qualification time is now around one day, a 5:1 cycle time improvement substantially reducing impact on design schedules. Some IP problems do escape past the qualification process and are ultimately detected in verification or implementation. But here’s the important point: the impact of escapes in the new flow is not noticeably different from the impact of escapes they had when using qualification based on the production tools. In addition, the new flow is substantially easier to maintain since the test fixturing for each class of test is significantly simpler to setup than it was for the production tools. Figure 3 illustrates a high-level view of this flow.

Maxwell’s equations suggest that for an antenna to radiate efficiently its dimensions must be comparable to a signal wavelength (usually 0.1? or greater). For example, a 1 MHz signal has a wavelength of 300 m (almost 1000 feet). Thus an antenna with dimensions appropriate for efficient radiation at that frequency would be impractical for many wireless applications.

In contrast, a 1 GHz signal has a wavelength of 0.1 m (less than 1 foot). This leads to a practical constraint on any personal communication system; that is, it must operate in a frequency band high enough to allow the efficient transmission and reception of electromagnetic waves using antennas of manageable size.

An electromagnetic wave of a specific frequency provides the link or channel between transmitting and receiving antennas located at the endpoints, over which information will be conveyed. This electromagnetic wave is called a carrier, and as discussed in Chapter 1, information is conveyed by modulating the carrier using a signal that represents the information.

Isotropic Radiation

Development of the range equation is greatly facilitated by considering how a signal propagates in free space. By free space” we mean a perfect vacuum with the closest object being infinitely far away. This ensures that the signal is not affected by resistive losses of the medium or objects that might otherwise reflect, refract, diffract, or absorb the signal. Although describing the propagation environment as free space may not be realistic, it does provide a useful model for understanding the fundamental properties of a radiated signal.