vcsvgenie 1.0.0

Programmatic Interpretations of Cadence Virtuoso Waveform VCSV files

VCSV Genie

This repo details methods for parsing transient simulation VCSV files from Cadence Virtuoso.

Installation

1. Clone this repo

git clone https://github.com/FSharp4/vcsvgenie.git
cd vcsvgenie

2. Build this repo

pip install build
python -m build

3. Install the repo

pip install -e .

4. Copy over a vcsv file of your choice, and use vcsvgenie to process the file, extracting propagation delays, printing traces, etc.

Example files can be seen in vcsvgenie/_dev.

Remark: In order for VCSV genie to automatically parse your file for propagations, you need to specify which signals are 'outputs' and which signals are inputs.

• Signals must be 'outputs' for propagation delay calculations to be performed on them with respect to inputs.
• Within Virtuoso Schematics/Layouts, these signals may be inputOutput or intermediate.

To specify which signals are inputs/outputs, supply lists of signal names to the input and output constructor arguments for TransientResultSpecification. (See Usage for an example).)

Functionality

VCSV Genie is presently limited to processing VCSV files produced from transient simulaitons in Cadence Virtuoso. Users can:

• Create pandas dataframes from VCSV files
• Create $(x, y)$ dataseries of individual waveforms (note that all timestamp $x$ vectors are the same in a VCSV file, and are not uniformly spaced)
• Create collections of waveforms from specifications (vcsvgenie.transient_waveform.TransientResult)
• Recognize signal buses using caret notation (i.e., A<3:0>) by specifying the individual signals in the TransientResultSpecification
• Digitize and tabulate signal bus data

Usage

General Example:

from pathlib import Path
from pprint import pprint

from vcsvgenie.read import read_vcsv
from vcsvgenie.transient_waveform import TransientResultSpecification, average_propagation_delays_by_category, maximum_propagation_delays_by_category, construct_waveforms

path = Path("example.vcsv")
dataframe, titles = read_vcsv(path)
waveforms = construct_waveforms(dataframe, titles)

specification = TransientResultSpecification(
    inputs = [
        '/A<3>', '/A<2>', '/A<1>', '/A<0', '/B<3>', '/B<2>', '/B<1>', '/B<0>', 'Clk'
    ],
    outputs = ['/z<7>', '/z<6>', '/z<5>', '/z<4>', '/z<3>', '/z<2>', '/z<1>', '/z<0>'],
    clock_period = 1e-9,
    logic_threshold = 0.5 # volts
)

results = specification.interpret(waveforms)
results.find_transitions()
results.find_propagations()

averages = average_propagation_delays_by_category(results.propagations)
pprint(results.propagations)
pprint(averages)

maxima = maximum_propagation_delays_by_category(results.propagations)
pprint(maxima)

results.digitize()
results.resolve_buses()
bus_data = results.tabulate_bus_data()
bus_data.to_csv("bus_data.csv")

results.plot(separate=True)

Retrieving observed worst-case delays:

from pprint import pprint

n = 10 # delays to retrieve
quasicritical_delays = transient_waveform.quasicritical_propagation_delays(results.propagations, n)
pprint([str(quasicritical) for quasicritical in quasicriticals])

Estimating global worst-case delays (in simulations on random inputs):

maxtpd_idx, maxtpd = transient_waveform.find_max_delay_trend(results.delays)
transient_waveform.plot_max_delay_trend(maxtpd_idx, maxtpd, show=True)

global_estimation = transient_waveform.estimate_global_critical_delay(maxtpd_idx, maxtpd, thres_samp=thres_samp)

Dependencies

• numpy
• matplotlib
• pandas
• sortedcontainers
• (optional): jupyter