Cadence® system design and verification solutions, integrated under our Verification Suite, provide the simulation, acceleration, emulation, and management capabilities.
Verification Suite Related Products A-Z
Cadence® digital design and signoff solutions provide a fast path to design closure and better predictability, helping you meet your power, performance, and area (PPA) targets.
Full-Flow Digital Solution Related Products A-Z
Cadence® custom, analog, and RF design solutions can help you save time by automating many routine tasks, from block-level and mixed-signal simulation to routing and library characterization.
Overview Related Products A-Z
Driving efficiency and accuracy in advanced packaging, system planning, and multi-fabric interoperability, Cadence® package implementation products deliver the automation and accuracy.
Cadence® PCB design solutions enable shorter, more predictable design cycles with greater integration of component design and system-level simulation for a constraint-driven flow.
An open IP platform for you to customize your app-driven SoC design.
Comprehensive solutions and methodologies.
Helping you meet your broader business goals.
A global customer support infrastructure with around-the-clock help.
More Support Log In
24/7 Support - Cadence Online Support
Locate the latest software updates, service request, technical documentation, solutions and more in your personalized environment.
Cadence offers various software services for download. This page describes our offerings, including the Allegro FREE Physical Viewer.
The Cadence Academic Network helps build strong relationships between academia and industry, and promotes the proliferation of leading-edge technologies and methodologies at universities renowned for their engineering and design excellence.
Participate in CDNLive
A huge knowledge exchange platform for academia to network with industry. We are looking for academic speakers to talk about their research to the industry attendees at the Academic Track at CDNLive EMEA and Silicon Valley.
Come & Meet Us @ Events
A huge knowledge exchange platform for academia. We are looking for academic speakers to talk about their research to industry attendees.
Americas University Software Program
Join the 250+ qualified Americas member universities who have already incorporated Cadence EDA software into their classrooms and academic research projects.
EMEA University Software Program
In EMEA, Cadence works with EUROPRACTICE to ensure cost-effective availability of our extensive electronic design automation (EDA) tools for non-commercial activities.
Apply Now For Jobs
If you are a recent college graduate or a student looking for internship. Visit our exclusive job search page for interns and recent college graduate jobs.
Cadence is a Great Place to do great work
Learn more about our internship program and visit our careers page to do meaningful work and make a great impact.
Get the most out of your investment in Cadence technologies through a wide range of training offerings.
Overview All Courses Asia Pacific EMEANorth America
Instructor-led training [ILT] are live classes that are offered in our state-of-the-art classrooms at our worldwide training centers, at your site, or as a Virtual classroom.
Online Training is delivered over the web to let you proceed at your own pace, anytime and anywhere.
Exchange ideas, news, technical information, and best practices.
The community is open to everyone, and to provide the most value, we require participants to follow our Community Guidelines that facilitate a quality exchange of ideas and information.
It's not all about the technology. Here we exchange ideas on the Cadence Academic Network and other subjects of general interest.
Cadence is a leading provider of system design tools, software, IP, and services.
Get email delivery of the Cadence blog featured here
1.1: (defun walkCvHier (cv consume)1.2: (foreach inst cv~>instances1.3: (walkCvHier inst~>master consume))1.4: (consume cv))
Just to reiterate: this function is naive for demonstration
purposes. You can easily imagine ways of enhancing or generalizing
this function to be more robust and handle more complicated hierarchy
descent. I encourage the reader to experiment with such enhancements,
and feel free to post your suggestions, or questions in the comment
section of this blog.
In the following paragraphs, I'll show more advanced uses of the
walkCvHier function. Please assume that all the
code in this article is SKILL++ which means it is defined in a source
file with a .ils extension.
In the previous article I showed how to implement
the reportCvHier by writing a private, global
function _reportCv. It turns out there are better ways
of doing this in SKILL++ which don't require polluting the global
function name space. It is not necessary to make this client function
visible to the world simply to pass it as a parameter
If you are using IC 6.1.5, a more explicit way of defining and
referencing private (local) functions is to use the newly available
4.1: (defun reportCvHier (top_cv)4.2: (flet ((reportCv (cv)4.3: (println (list cv~>libName4.4: cv~>cellName4.5: cv~>viewName))))4.6: (walkCvHier top_cv reportCv)))
The flet macro may be used to define one or more local
functions which are thereafter available for reference by name inside
the body of the flet. Such a by-name reference is shown
on line 4.6 in the example above, where the local function
named reportCv is only visible and callable within the
body of the flet form (i.e., within the matching
parentheses which surround flet). This local function
has a single required argument named cv. Since this is
SKILL++, we are free to use function names (global or local) as if
they were variable names, as on line 4.6.
One power of local functions over global functions is their ability to
securely reference other lexical variables which are in scope. For
example, it would be possible for the local
function reportCv to reference the
variable top_cv if necessary. Because reportCv lies textually within the matching
parentheses of the (defun reportCvHier ...) form, it is
free to use all the local variables which that form binds, including
the formal parameter of top_cv. Other examples of these are
shown on lines 6.4 and 7.4 below.
(defun reportCvHier ...)
If you don't have access to IC 6.1.5, you may use the following
construct. In this case we simply create a function on-the-fly
without a name, and pass it directly to reportCvHier.
5.1: (defun reportCvHier (cv)5.2: (walkCvHier cv (lambda (cv)5.3: (println (list cv~>libName5.4: cv~>cellName5.5: cv~>viewName)))))
In the next example (lines 6.1-6.5) I'd like to
use walkCvHier to count the number of cellViews in the
hierarchy, and in (7.1-7.5) build a table mapping each cellView to the
number of times it occurs in the hierarchy. In this case the consumer
function passed to walkCvHier needs to modify some state
(such as, a counter or a hash table) within the calling function.
How would you attack this with traditional SKILL? With traditional
SKILL the consumer function would probably need to modify a global
variable to maintain the count, introducing two immediately apparent
problems. 1) the function would probably not be non-reentrant.That is,
you may not be able to pass in consumer function which itself
calls walkCvHier. 2) If the consumer function encounters
an error, this global variable could be left in an incomplete state,
which would confuse subsequent calls.
Why do these problems vanish with the SKILL++ approach? Because
there is no need for the global variable. The variable can remain
local and lexical as seen on lines 6.2 and 7.2.
This function countCvHier passes a function
to walkCvHier which increments a
variable, occurrences, at each step in the hierarchy. Note
that the walkCvHier knows absolutely nothing about the
local variable occurrences. Moreover, even
if walkCvHier were implemented having a local variable of
the same name it would not have any ill
effect on countCvHier.
6.1: (defun countCvHier (cv)6.2: (let ((occurrences 0))6.3: (walkCvHier cv (lambda (cv)6.4: occurrences++))6.5: occurrences))
In the function occureHier, we use the same technique to
pass a function to walkCvHier which modifies a hash table
referenced by the local variable, occurrences.
7.1: (defun occureHier (cv)7.2: (let ((occurrences (makeTable 'occur 0)))7.3: (walkCvHier cv (lambda (cv)7.4: occurrences[cv] = (add1 occurrences[cv])))7.5: occurrences))
It is perhaps worth emphasizing that if you were using traditional
SKILL rather than SKILL++, this technique would fail for several
reasons. These problems boil down in the end to differences between
dynamic and global variables. While dynamic variables are interesting
and have powerful uses, this is not one such place to use them.
Without giving a detailed explanation of subtle errors some of the
problems are the following:
If you want to purge all cellViews in a design hierarchy you might
think of evaluating something like (walkCvHier cv
dbPurge), but that probably wouldn't be a good idea. Why?
Well for one reason, the same cellView might (and probably does) occur
multiple times. You don't want cellViews being purged while they are
still needed for traversal. We need to call dbPurge
iteratively over a list of cellViews where each cellView occurs only
once and children always precede parents. Here is a function which
will do that.
8.1: (defun purgeCvHier (cv)8.2: (let ((visited nil))8.3: (walkCvHier cv (lambda (cv)8.4: (unless (memq cv visited)8.5: (push cv visited))))8.6: (mapc dbPurge (reverse visited))))
How does it work? Neither the consumer function nor consequently the
traversal function walkCvHier actually purge anything,
but rather the consumer function collects a list of cellViews that
are visited. Since the walkCvHier is written such that
it descends into children cellViews before applying the consumer
function to the parent, purgeCvHier simply uses the
SKILL push macro to build a list in reverse order, so
that parents come before children -- a small problem that is easily
resolved with a call to reverse.
A more robust traversal implementation of walkCvHier
function could of course allow two consumer functions to be used -- one
which is applied before traversing into children cellViews, and a
second to be called after each step of the descent is finished. You,
the reader, might consider implementing a function. Alternatively,
you could provide an optional argument to walkCvHier
which specifies whether the consumer function will be called before or
after the descent.
There is one important restriction on local functions defined
with flet: they are not allowed to call themselves
recursively, and if you define more than one local function in a
single flet, they are not allowed to call each other.
You might initially think this is a limitation, but actually it turns
out to be a powerful feature which we can look at in a future posting. If anyone is interested, please ask in the comment section of this
If you really need to call a local function recursively, or you need
to write several local functions, some of which call other ones, you
can use labels instead. Its syntax is exactly the same
as flet, but its scoping rules are different.
Some other Lisp dialects provide implementations of flet
and labels. The ones I'm aware of are
What you've seen above are some basics and a few more advanced
features of SKILL++. Using local functions makes your SKILL++ code
more self-contained without sacrificing modularity. And it's pretty
easy to use SKILL++ on practical, easy to understand problems.
We'll look at some more examples in future articles.
Hi Rahul, thanks for the question. I'll try to answer your question, but please respond with a follow up if my answer is not clear of sufficient.
Line 4.6 is a call to the function walkCvHier with two arguments, top_cv and reportCv. The value of top_cv is the cellView at the top of the hierarchy where we want the hierarchy traversal to commence. The value of reportCv is a function; in fact it is a unary function (a function of a single argument). Whoever calls reportCv is responsible to pass a valid cellView for it to operate on.
Now take a look at at the code for walkCvHier. Its second argument is consume. So inside the walkCvHier function, the value of the consume variable is actually the same value as reportCv from line 4.6. Line 1.4 calls the function, effectively reportCv, with a valid cellView which is obtained by inst~>master. That is where the cellView comes from which you were asking about.
In 4.6 above why cv variable was not passed to reportCv. flet function , and how the reportCv local function knows abotu cv to operate on