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From: Pete Fraser on 7 Aug 2010 21:26
I'm new to Matlab, and to IIR design, so
sorry in advance if these questions are dumb.

I've been playing with filterbuilder and FDATool
just to get familiar with the options / design flow.
There seems to be a lot of overlap.

Why would somebody want to use filterbuilder rather
the FDATool?

Say I design a 6th order, DFII Butterworth LPF,
with Fs = 48000 and Fc = 10800.

I do a fixed point quantization with the default settings.
Input word length is 16, and input fraction length 15.

I assume that means that the input is considered to run
from -1.0 to +(2^15 -1)/2^15. Is that correct?

When the "avoid overflow" box on the output is checked,
it seems to operate as a 16 bit output with a fraction length
of ten bits. Why is that? I would have assumed that, if the
input is considered to have one bit before the point, then
the output would need two bits to accommodate
overshoots in the step response. Why are six bits needed?

I'm not sure what the Magnitude Response Estimate does.
I'm guessing it shows the difference between a white
pseudo-noise input and the associated ouput spectrum.
The response in the stop-band is a combination of the actual
frequncy resposne, and the stop-band noise caused by
pass-band components being quantized. Is that right?

Thanks

Pete




From: Steve Pope on 7 Aug 2010 22:06
Pete Fraser <pfraser(a)covad.net> wrote:

>I'm new to Matlab, and to IIR design, so
>sorry in advance if these questions are dumb.
>
>I've been playing with filterbuilder and FDATool
>just to get familiar with the options / design flow.
>There seems to be a lot of overlap.
>
>Why would somebody want to use filterbuilder rather
>the FDATool?
>
>Say I design a 6th order, DFII Butterworth LPF,
>with Fs = 48000 and Fc = 10800.
>
>I do a fixed point quantization with the default settings.
>Input word length is 16, and input fraction length 15.
>
>I assume that means that the input is considered to run
>from -1.0 to +(2^15 -1)/2^15. Is that correct?
>
>When the "avoid overflow" box on the output is checked,
>it seems to operate as a 16 bit output with a fraction length
>of ten bits. Why is that? I would have assumed that, if the
>input is considered to have one bit before the point, then
>the output would need two bits to accommodate
>overshoots in the step response. Why are six bits needed?
>
>I'm not sure what the Magnitude Response Estimate does.
>I'm guessing it shows the difference between a white
>pseudo-noise input and the associated ouput spectrum.
>The response in the stop-band is a combination of the actual
>frequncy resposne, and the stop-band noise caused by
>pass-band components being quantized. Is that right?

I guess my first recommendation would be to use fdatool to generate
the (full precision) filter coefficients, then do your own quantization
study from scratch. This will avoid putting you in the position
of having to guess what the tool is doing.


Steve
From: Pete Fraser on 8 Aug 2010 17:56
"Steve Pope" <spope33(a)speedymail.org> wrote in message
news:i3n53q$9sq$1(a)blue.rahul.net...

> Back to the OP's problem -- it occurs to me that if he is intending to
> use fdatool's design path through to HDL generation,

I'd be prepared to use the tool's HDL generation, but assumed
that it probably wouldn't do what I needed. I have a relatively low
sample rate, so I was just going to use a pool of memory and a MAC,
and put all the complexity in the memory control. I can't find
any way of making the Matlab HDL generator do that
(nor did I really expect to).

> then he is
> probably stuck figuring out how the tool's fixed-point modes work,
> thus making the advice given in this thread of limited value.
>
> (But I would still do a grounds-up quantization study independent of
> the tool even in this case, approaching it as I outlined in
> response to OP's previous question.)

I think I'll have to do that. I just need to get some familiarity with
Matlab first (any paper / text you could recommend on doing a
grounds up quantization study on an IIR?).

It seemed to me that the FDATool pretty much did what I needed,
but as I began to work with it I realized some of the answers I was
getting were counter-intuitive. For relatively wide LPFs, the default
quantization choices seem to be reasonable. The noise analysis
shows about 60 dB stop-band artifacts, but I can increase
accuracy at various points in the filter, and get the performance I need.

It's when I try to understand what the various controls mean that I
get a bit frustrated. The documentation just expands slightly on
the titles of the boxes, but doesn't really explain anything.
For example, the default 6th order Butterworth LPF has an input
of 16-bits with 15 fractional, and an output of 16-bits with 10 fractional.
Why would I need anything more than two bits before the binary
pont on the output? Clearly I don't understand what their
nomenclature means, but I can't find any detailed information (I know
I've repeated myself with a previous question, but I'm hoping
someone on the matlab group might answer).

Things get worse when I go to higher accuracy and narrower filters.
If I start looking at a filter with f3db = 0.05, things get messy. The
response
is ugly, the stop-band artifacts are extremely high, and the tool does
things I don't expect. It doesn't seem to default to higher accuracy in the
data path for narrower filters, but when I change to higher accuracy the
performance gets much worse. I'm not sure if the tool is broken, I'm
ignorant, or a combination of the two (wierd rendering bugs in the GUI don't
fill me with confidence though.).

I seem to get bad results with any architecture. DF2SOS is weird, but
I tried Steve's earlier suggestion of ARMA, and converted a 6th
order Butterworth LPF with f3db = 0.05 to ARMA. The floating
point response looks fine, but converting to fixed point with the
tool's defaults gives me a Magnitude Response Estimate of about -80 dB
from DC to Nyquist. Doubling the number of bits everywhere gives me
an MRE of -140 dB from DC to Nyquist (note, this is Magnitude
Response Estimate -- not Noise Power Spectrum).

I've also got to decide whether / what I'm buying from Mathworks
(the current system is a loaner). I started off liking its power, but am
now thinking it's either buggy or I'm too stupid to use it.

Thanks

Pete




From: Steve Pope on 8 Aug 2010 23:52
Pete Fraser <pfraser(a)covad.net> wrote:

>Things get worse when I go to higher accuracy and narrower filters.
>If I start looking at a filter with f3db = 0.05, things get messy. The
>response
>is ugly, the stop-band artifacts are extremely high, and the tool does
>things I don't expect. It doesn't seem to default to higher accuracy in the
>data path for narrower filters, but when I change to higher accuracy the
>performance gets much worse. I'm not sure if the tool is broken, I'm
>ignorant, or a combination of the two (wierd rendering bugs in the GUI don't
>fill me with confidence though.).

>I seem to get bad results with any architecture. DF2SOS is weird, but
>I tried Steve's earlier suggestion of ARMA, and converted a 6th
>order Butterworth LPF with f3db = 0.05 to ARMA. The floating
>point response looks fine, but converting to fixed point with the
>tool's defaults gives me a Magnitude Response Estimate of about -80 dB
>from DC to Nyquist. Doubling the number of bits everywhere gives me
>an MRE of -140 dB from DC to Nyquist (note, this is Magnitude
>Response Estimate -- not Noise Power Spectrum).

I don't necessarily see the above as bad results, other than the
non-controllability of the tool, but I do not know your dynamic
range requirements. Maybe you have extreme requirements and will
need to use floating point arithmetic in your implementation;
that sometimes happens.

The last time I had to design a comparable Butterworth filter, it required
a dynamic range of about 80 dB, and I ended up with the following
precisions in a lattice (ARMA) structure: 22 bit internal data, and 12
bit coefficients. This was a 4th order filter with a passband
of 0.01 * Fs. The internal data values required four additional
bits to the left of the binary point than did the input value.
This seems (roughly) comparable to what you're trying to do.

As I mentioned, I like to plot RMS error (in dBc) vs. input signal level
(in dB relative to some nominal level) to visualize quantization
effects. Such a plot should typically have a flat floor in the middle of
the usable range (representing the limits of your coefficient
quantization; this includes your stop-band artifacts); it should have a
6 dB/octave slope in the lower-signal range (resulting from the data-path
quantization); and a steep saturating effect at high signal levels. If
you do not obtain this sort of U-shaped or V-shaped curve, something is
seriously wrong.

(I do not think this is one of the plots fdatool likes to spit out on
its own.)


Steve
From: Steve Pope on 8 Aug 2010 23:55
Steve Pope <spope33(a)speedymail.org> wrote:

>it should have a
>6 dB/octave slope in the lower-signal range (resulting from the data-path
>quantization);

Oops.. meant to say a slope of one (6dB per 6dB).


S.
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