Latest your news

your

Know Your Delhi Candidate: Kapil Mishra

By www.ndtv.com
Published On :: Sun, 09 Feb 2020 17:00:59 +0530

Kapil Mishra is a former Aam Aadmi Party (AAP) leader-turned BJP candidate for the Delhi election. When he was with the AAP, Mr Mishra was elected from Delhi's Karawalnagar seat in the 2014 Delhi...

Full Article

People

your

Know Your Delhi Candidate: Arvinder Singh Lovely

By www.ndtv.com
Published On :: Sun, 09 Feb 2020 18:29:05 +0530

Arvinder Singh Lovely is a Congress leader who had walked out of his party in 2017 to join the BJP. He returned to the Congress in February 2018. Mr Lovely, who has been the Delhi Education Minister...

Full Article

People

your

Know Your Delhi Candidate: Satyendar Jain

By www.ndtv.com
Published On :: Sun, 09 Feb 2020 19:18:30 +0530

Satyendar Jain is an Aam Aadmi Party (AAP) leader and a minister in Arvind Kejriwal's cabinet. He holds multiple portfolios including the Health Ministry.

Full Article

People

your

Know Your Delhi Candidate: Raghav Chadha

By www.ndtv.com
Published On :: Tue, 11 Feb 2020 10:51:09 +0530

Aam Aadmi Party (AAP) leader Raghav Chadha, party's candidate from central Delhi's Rajinder Nagar, is pitted against BJP veteran RP Singh, a 58-year-old advertising professional from the same...

Full Article

People

your

CONVERSATIONS-Share your success stories

By www.banknetindia.com
Published On ::

Banknet Group will publish stories of achievers, entrepreneurs from BFSI, IT

Full Article

your

How To Customise Your Car Insurance Policy?

By www.banknetindia.com
Published On ::

How To Customise Your Car Insurance Policy?

Full Article

your

Ensure a motivated labour force in your business

By www.banknetindia.com
Published On ::

How to ensure a highly motivated labour force in your business?

Full Article

your

Wellness is directly proportionate to your health insurance

By www.banknetindia.com
Published On ::

How your wellness is directly proportionate to your health insurance premium?

Full Article

your

Players getting paid? Video games returning? Answering your NCAA name, image and likeness questions

By www.espn.com
Published On :: Thu, 30 Apr 2020 09:39:52 EST

The NCAA supports a proposal allowing college athletes to sign endorsement deals and receive money for other work, as long as their schools aren't involved. What does this mean for college athletics -- and its beloved football video game?

Full Article

your

BoardSurfers: Allegro In-Design Impedance Analysis: Screen your Routed Design Quickly

By community.cadence.com
Published On :: Tue, 28 Apr 2020 13:12:00 GMT

Have you ever manufactured a printed circuit board (PCB) without analyzing all the routed signal traces? Most designers will say “yes, all the time.” Trace widths and spacing are set by constraints,...

[[ Click on the title to access the full blog on the Cadence Community site. ]]

Full Article

your

Start Your Engines: AMSD Flex – Your Instant Access to Latest Spectre Features!

By community.cadence.com
Published On :: Fri, 01 May 2020 06:59:00 GMT

Cadence ® Spectre ® AMS Designer is a high-performance mixed-signal simulation system. The ability to use multiple engines, and drive from a variety of platforms enables you to "rev...

[[ Click on the title to access the full blog on the Cadence Community site. ]]

Full Article

your

Tales from DAC: Altair's HERO Is Your Hero

By feedproxy.google.com
Published On :: Mon, 29 Jul 2019 21:07:00 GMT

Emulators are great. They vastly speed up verification to the point where it’s hard to imagine life without them; as designs grow in complexity, simple simulation can’t keep up for the biggest designs. The extra oomph from emulation is almost a necessity for the top percentages of design sizes. However, many users of Palladium aren’t efficiently using their unit’s processing power, and as a result they’re missing out on the full speed-up potential that Palladium can provide.

Altair’s HERO is here for you. With its help, your Palladium unit can be even more amazing for your productivity than before.

HERO (that’s Hardware Emulator Resource Optimizer) adds emulator support to Altair’s Accelerator. You already know and love Altair’s scheduling tools; so why not make them do more for you, so you can be one of those people who are making the most out of their Palladium system?

Emulators are kind of like big computers, but it’s a lot harder to manage leftover resources on an emulator than it is on, say, a CPU. A scheduler like HERO neatly sidesteps this problem by more intelligently using the resources available to ensure that there’s a minimal patchwork of leftover resources to begin with.

HERO supports past generations of Palladium as well, so if you’re still using an older version, you can still take advantage of the upgrades HERO provides. There’s a wide variety of features HERO has that make your emulator easier to use. HERO separates a job into a “select” section and a “run” section: the “select” part makes a last-minute decision on which domains or boards to use, while the “run” part is the actual job. This makes it easier to ensure that your Palladium emulator is being used as efficiently as possible. Jobs are placed using “shapes”, which are a set of job types; these can be selected from a list of pre-defined ones by the user. Shapes can have special constraints if those are needed.

A new reservation system also helps HERO organize Palladium’s processing power better. HERO offers both “hard” reservations and “soft” reservations. A hard reservation locks other users out of reserving any part of the emulator at all, while a soft reservation allows a user to reserve a part of the emulator for a later use. Think of it like this: a soft reservation is like grabbing a ticket from the deli counter, while a hard reservation stops you from ever entering the market.

When using HERO, you can manage your entire verification workload. You’ll find that your utilization of your emulator vastly increases—it’s been reported that some users using only 30% of the capabilities of their Palladium unit(s) saw a massive increase to over 90% once they made the switch to HERO.

If you’re ready to take your Palladium productivity to the next level, Altair has a HERO for you.

To see the full presentation given by Andrea Casotto in the Cadence Theater at DAC 2019, check here.

Full Article

your

Specman: Analyze Your Coverage with Python

By feedproxy.google.com
Published On :: Wed, 06 Nov 2019 13:31:00 GMT

In the former blog about Python and Specman: Specman: Python Is here!, we described the technical information around Specman-Python integration. Since Python provides so many easy to use existing libraries in various fields, it is very tempting to leverage these cool Python apps.

Coverage has always been the center of the verification methodology, however in the last few years it gets even more focus as people develop advanced utilities, usually using Machine Learning aids. Anyhow, any attempt to leverage your coverage usually starts with some analysis of the behavior and trends of some typical tests. Visualizing the data makes it easier to understand, analyze, and communicate. Fortunately, Python has many Visualization libraries.

In this blog, we show an example of how you can use the plotting Python library (matplotlib) to easily display coverage information during a run. In this blog, we use the Specman Coverage API to extract coverage data, and a Python module to display coverage grades interactively during a single run and the way to connect both.

Before we look at the example, if you have read the former blog about Specman and Python and were concerned about the fact that python3 is not supported, we are glad to update that in Specman 19.09, Python3 is now supported (in addition to Python2).

The Testcase
Let’s say I have a stable verification environment and I want to make it more efficient. For example: I want to check whether I can make the tests shorter while hardly harming the coverage. I am not sure exactly how to attack this task, so a good place to start is to visually analyze the behavior of the coverage on some typical test I chose. The first thing we need to do is to extract the coverage information of the interesting entities. This can be done using the old Coverage API.

Coverage API
Coverage API is a simple interface to extract coverage information at a certain point. It is implemented through a predefined struct type named user_cover_struct. To use it, you need to do the following:

Define a child of user_cover_structusing like inheritance (my_cover_struct below).
Extend its relevant methods (in our example we extend only the end_group() method) and access the relevant members (you can read about the other available methods and members in cdnshelp).
Create an instance of the user_cover_structchild and call the predefined scan_cover() method whenever you want to query the data (even in every cycle). Calling this method will result in calling the methods you extended in step 2.

The code example below demonstrates these three steps. We chose to extend the end_group() method and we keep the group grade in some local variable. Note that we divide it by 100,000,000 to get a number between 0 to 1 since the grade in this API is an integer from 0 to 100,000,000.

struct my_cover_struct like user_cover_struct {
!cur_group_grade:real;

//Here we extend user_cover_struct methods
end_group() is also {

cur_group_grade = group_grade/100000000;
}
};

extend sys{
!cover_info : my_cover_struct;

run() is also {
start monitor_cover ();
};

monitor_cover() @any is {
cover_info = new;

while(TRUE) {

// wait some delay, for example –
wait [10000] * cycles;

// scan the packet.packet_cover cover group
compute cover_info.scan_cover("packet.packet_cover");
};//while
};// monitor_cover
};//sys

Pass the Data to a Python Module
After we have extracted the group grade, we need to pass the grade along with the cycle and the coverage group name (assuming there are a few) to a Python module. We will take a look at the Python module itself later. For now, we will first take a look at how to pass the information from the e code to Python. Note that in addition to passing the grade at certain points (addVal method), we need an initialization method (init_plot) with the number of cycles, so that the X axis can be drawn at the beginning, and end_plot() to mark interesting points on the plot at the end. But to begin with, let’s have empty methods on the Python side and make sure we can just call them from the e code.

# plot_i.py
def init_plot(numCycles):
print (numCycles)
def addVal(groupName,cycle,grade):
print (groupName,cycle,grade)
def end_plot():
print ("end_plot")

And add the calls from e code:

struct my_cover_struct like user_cover_struct {

@import_python(module_name="plot_i", python_name="addVal")
addVal(groupName:string, cycle:int,grade:real) is imported;

!cur_group_grade:real;

//Here we extend user_cover_struct methods
end_group() is also {
cur_group_grade = group_grade/100000000;

//Pass the values to the Python module
addVal(group_name,sys.time, cur_group_grade);

} //end_group
};//user_cover_struct

extend sys{

@import_python(module_name="plot_i", python_name="init_plot"
init_plot(numCycles:int) is imported;

@import_python(module_name="plot_i", python_name="end_plot")
end_plot() is imported;

!cover_info : my_cover_struct;

run() is also {
start scenario();
};

scenario() @any is {

//initialize the plot in python
init_plot(numCycles);

while(sys.time<numCycles)
{
//Here you add your logic

//get the current coverage information for packet
cover_info = new;
var num_items:= cover_info.scan_cover("packet.packet_cover");

//Here you add your logic

};//while

//Finish the plot in python
end_plot();

}//scenario
}//sys

The green lines define the methods as they are called from the e
The blue lines are pre-defined annotations that state that the method in the following line is imported from Python and define the Python module and the name of the method in it.
The red lines are the calls to the Python methods.

Before running this, note that you need to ensure that Specman finds the Python include and lib directories, and Python finds our Python module. To do this, you need to define a few environment variables: SPECMAN_PYTHON_INCLUDE_DIR, SPECMAN_PYTHON_LIB_DIR, and PYTHONPATH.

The Python Module to Draw the Plot
After we extracted the coverage information and ensured that we can pass it to a Python module, we need to display this data in the Python module. There are many code examples out there for drawing a graph with Python, especially with matplotlib. You can either accumulate the data and draw a graph at the end of the run or draw a graph interactively during the run itself- which is very useful especially for long runs.

Below is a code that draws the coverage grade of multiple groups interactively during the run and at the end of the run it prints circles around the maximum point and adds some text to it. I am new to Python so there might be better or simpler ways to do so, but it does the work. The cool thing is that there are so many examples to rely on that you can produce this kind of code very fast.

# plot_i.py
import matplotlib
import matplotlib.pyplot as plt

plt.style.use('bmh')

#set interactive mode
plt.ion()

fig = plt.figure(1)
ax = fig.add_subplot(111)

# Holds a specific cover group
class CGroup:

    def __init__(self, name, cycle,grade ):
        self.name = name
        self.XCycles=[]
        self.XCycles.append(cycle)
        self.YGrades=[]
        self.YGrades.append(grade)
        self.line_Object= ax.plot(self.XCycles, self.YGrades,label=name)[-1]
        self.firstMaxCycle=cycle
        self.firstMaxGrade=grade

    def add(self,cycle,grade):
        self.XCycles.append(cycle)
        self.YGrades.append(grade)
        if grade>self.firstMaxGrade:
            self.firstMaxGrade=grade
            self.firstMaxCycle=cycle
self.line_Object.set_xdata(self.XCycles)
        self.line_Object.set_ydata(self.YGrades)

plt.legend(shadow=True)
fig.canvas.draw()

#Holds all the data of all cover groups
class CData:
groupsList=[]

def add (self,groupName,cycle,grade):
found=0

for group in self.groupsList:
            if groupName in group.name:
                group.add(cycle,grade)
                found=1
                break

if found==0:
obj=CGroup(groupName,cycle,grade)
self.groupsList.append(obj)

    def drawFirstMaxGrade(self):
for group in self.groupsList:
            left, right = plt.xlim()
            x=group.firstMaxCycle
            y=group.firstMaxGrade

            #draw arrow
            #ax.annotate("first maximum grade", xy=(x,y),
            #xytext=(right-50, 0.4),arrowprops=dict(facecolor='blue', shrink=0.05),)

#mark the points on the plot
plt.scatter(group.firstMaxCycle, group.firstMaxGrade,color=group.line_Object.get_color())

#Add text next to the point
            text='cycle:'+str(x)+' grade:'+str(y)
plt.text(x+3, y-0.1, text, fontsize=9, bbox=dict(boxstyle='round4',color=group.line_Object.get_color()))

#Global data
myData=CData()

#Initialize the plot, should be called once
def init_plot(numCycles):
    plt.xlabel('cycles')
    plt.ylabel('grade')
    plt.title('Grade over time')
    plt.ylim(0,1)
    plt.xlim(0,numCycles)

#Add values to the plot
def addVal(groupName,cycle,grade):
myData.add(groupName,cycle,grade)

#Mark interesting points on the plot and keep it shown
def end_plot():
plt.ioff();
myData.drawFirstMaxGrade();

#Make sure the plot is being shown
plt.show();

#uncomment the following lines to run this script with simple example to make sure #it runs properly regardless of the Specman interaction

#init_plot(300)
#addVal("xx",1,0)
#addVal("yy",1,0)
#addVal("xx",50,0.3)
#addVal("yy",60,0.4)
#addVal("xx",100,0.8)
#addVal("xx",120,0.8)
#addVal("xx",180,0.8)
#addVal("yy",200,0.9)
#addVal("yy",210,0.9)
#addVal("yy",290,0.9)
#end_plot()

In the example we used, we had two interesting entities: packet and state_machine, thus we had two equivalent coverage groups. When running our example connecting to the Python module, we get the following graph which is displayed interactively during the run.

When analyzing this specific example, we can see two things. First, packet gets to a high coverage quite fast and significant part of the run does not contribute to its coverage. On the other hand, something interesting happens relating to state_machine around cycle 700 which suddenly boosts its coverage. The next step would be to try to dump graphic information relating to other entities and see if something noticeable happens around cycle 700.

To run a complete example, you can download the files from: https://github.com/okirsh/Specman-Python/

Do you feel like analyzing the coverage behavior in your environment? We will be happy to hear about your outcomes and other usages of the Python interface.

Orit Kirshenberg
Specman team

Full Article

your

BoardSurfers: Allegro In-Design Impedance Analysis: Screen your Routed Design Quickly

By feedproxy.google.com
Published On :: Tue, 28 Apr 2020 13:12:00 GMT

Full Article

your

IC Packagers: Identify Your Components

By feedproxy.google.com
Published On :: Tue, 24 Mar 2020 14:19:00 GMT

We’ve all seen bar codes and the more modern QR codes. They’re everywhere you go – items at the grocery store, advertisements and posters, even on websites. Did you know that, with the productivity toolbox in Allegro Package Designe...(read more)

Full Article

Allegro Package Designer
Allegro PCB Editor

your

IC Packagers: You Can Leave Your (Molding) Cap On…

By feedproxy.google.com
Published On :: Tue, 21 Apr 2020 14:27:00 GMT

Molding caps aren’t something we talk about too frequently around here. We all know they exist, and they serve an important purpose of protecting the delicate die from potentially harsh environmental conditions. They impact how well heat can be...(read more)

Full Article

Allegro Package Designer

your

Welcome! Please use this forum to upload your code

By feedproxy.google.com
Published On :: Tue, 05 Aug 2008 21:01:43 GMT

Please include a brief summary of how to use it.

Full Article

your

Are You Stuck While Synthesizing Your Design Due to Low-Power Issues? We Have the Solution!

By feedproxy.google.com
Published On :: Tue, 31 Mar 2020 14:39:00 GMT

Optimizing power can be a very convoluted and crucial process. To make design chips meet throughput goals along with optimal power consumption, you need to plan right from the beginning! (read more)

Full Article

your

Start Your Engines: AMSD Flex—Take your Pick!

By feedproxy.google.com
Published On :: Thu, 16 Apr 2020 22:16:00 GMT

Introduction to AMSD Flex mode and its benefits.(read more)

Full Article

mixed signal design
AMS Designer
AMSD
AMSD Flex Mode
mixed-signal verification

your

Start Your Engines: AMSD Flex – Your Instant Access to Latest Spectre Features!

By feedproxy.google.com
Published On :: Fri, 01 May 2020 06:59:00 GMT

This blog talks about how to enable the AMS Designer flex mode.(read more)

Full Article

mixed signal design
AMS Designer
AMSD
AMSD Flex Mode
mixed-signal verification

your

Virtuosity: Are Your Layout Design Mansions Correct-by-Construction?

By community.cadence.com
Published On :: Thu, 26 Mar 2020 14:21:00 GMT

Do you want to create designs that are correct by construction? Read along this blog to understand how you can achieve this by using Width Spacing Patterns (WSPs) in your designs. WSPs, are track lines that provide guidance for quickly creating wires. Defining WSPs that capture the width-dependent spacing rules, and snapping the pathSegs of a wire to them, ensures that the wires meet width-dependent spacing rules.(read more)

Full Article

ICADVM18.1
Advanced Node
Layout Suite
width spacing patterns
Layout
Virtuoso
Virtuosity
usability
Custom IC Design
ux

your