Data-path accuracy in IIR filters?
in [FPGA]
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From: Pete Fraser on 29 Jul 2010 15:23 I am working on a project where I need to implement 6-th order Butterworth low-pass filters in an FPGA. In some the bandwidth is low relative to the input data rate, whereas others have higher bandwidth. I can use ScopeIIR or Matlab to give me a good idea of coefficient accuracy for any given ratio of bandwidth to input sample rate. However, I'm not sure what data-path accuracy I need (for 20-bit input / output accuracy). Is there a rule-of-thumb I can use, or do I just have to simulate the filter with real data and see what gives me low enough noise? I was planning on using biquads, but I'm not sure whether I'm better off with DF1 or DF2 sections. Thoughts? Thanks Pete
From: Vladimir Vassilevsky on 29 Jul 2010 15:37 Pete Fraser wrote: > I am working on a project where I need to > implement 6-th order Butterworth low-pass > filters in an FPGA. In some the bandwidth is > low relative to the input data rate, whereas > others have higher bandwidth. I can use ScopeIIR > or Matlab to give me a good idea of coefficient > accuracy for any given ratio of bandwidth to > input sample rate. > > However, I'm not sure what data-path accuracy > I need (for 20-bit input / output accuracy). > Is there a rule-of-thumb I can use, or do I just > have to simulate the filter with real data and > see what gives me low enough noise? > > I was planning on using biquads, but I'm not sure > whether I'm better off with DF1 or DF2 sections. If the filter cutoff frequency is much lower then samplerate, then loss of precision in the direct implementation of the biquad section could be very roughly estimated as ~ Q (Fc/Fs)^2. Let's say Fc = 100 kHz, Fs = 100 Hz, Q = 1. Loss of precision ~ 1e6 ~ 20 bits. That is, if your filter is implemented with 32 bit data path, the result will be accurate only to 12 bits. There are, of course, methods to get more accurate estimates and to improve precision, however this is a different and rather long story. Vladimir Vassilevsky DSP and Mixed Signal Design Consultant http://www.abvolt.com
From: Steve Pope on 29 Jul 2010 15:47 Pete Fraser <pfraser(a)covad.net> wrote: >I am working on a project where I need to >implement 6-th order Butterworth low-pass >filters in an FPGA. In some the bandwidth is >low relative to the input data rate, whereas >others have higher bandwidth. I can use ScopeIIR >or Matlab to give me a good idea of coefficient >accuracy for any given ratio of bandwidth to >input sample rate. > >However, I'm not sure what data-path accuracy >I need (for 20-bit input / output accuracy). >Is there a rule-of-thumb I can use, or do I just >have to simulate the filter with real data and >see what gives me low enough noise? You should simulate the fixed-point filter. When simulating, you do not necessarily have to stimulate it with realistic data. I often will stimulate the design being tested with bandlimited noise, and measure the RMS error of output (relative to the same design, but in full floating-point). Plotting the RMS error (in dBc) vs. RMS input level gives you a very good idea of the dynamic range of the fixed point design. >I was planning on using biquads, but I'm not sure >whether I'm better off with DF1 or DF2 sections. You can do this, or you can use a lattice topology (called "ARMA" in matlab/fdatool), which is the most well-behaved topology. Steve
From: robert bristow-johnson on 29 Jul 2010 16:12 On Jul 29, 3:23 pm, "Pete Fraser" <pfra...(a)covad.net> wrote: > I am working on a project where I need to > implement 6-th order Butterworth low-pass > filters in an FPGA. In some the bandwidth is > low relative to the input data rate, whereas > others have higher bandwidth. I can use ScopeIIR > or Matlab to give me a good idea of coefficient > accuracy for any given ratio of bandwidth to > input sample rate. > > However, I'm not sure what data-path accuracy > I need (for 20-bit input / output accuracy). > Is there a rule-of-thumb I can use, or do I just > have to simulate the filter with real data and > see what gives me low enough noise? > > I was planning on using biquads, but I'm not sure > whether I'm better off with DF1 or DF2 sections. > > Thoughts? i think you'll do better with DF1 sections (it will cost you two more storage states, you'll have 8 instead of 6) and, for each section, an accumulator that is wide enough to have no error given the word widths of the signal (you said 20 bits) and the coefficients (that might depend on the range of coefficients). using 1st-order error shaping, a.k.a. "fraction saving" might gain you something, and you can accomplish this for free if you leave in your accumulator (as an initial value) the long-word output from the previous sample. you will need to compensate this by subtracting 1 from "a1", the first feedback coefficient. then, for rounding to the next section, all you need to do is truncate the low-order bits of the word going to the next section, no rounding necessary (that gets fixed with the fraction saving). that means, for H(z) = N(z)/D(z) where D(z) = 1 + a1*z^(-1) + a2*z^(-2) = 1 + (a1+1)*z^(-1) - z^(-1) + a2*z^(-2) the term z^(-1) would be the double wide output from the previous sample, y[n-1]. if your biquads remain resonant (meaning complex conjugate poles) and if the resonant frequency is going to be very low and if the resonance will be high (that is the poles are close to z=1), then consider reworking the denominator of the biquad transfer function as: D(z) = 1 + a1*z^(-1) + a2*z^(-2) = 1 + (a1+2)*z^(-1) - 2*z^(-1) + (a2-1)*z^(-2) + z^(-2) for the terms 2*z^(-1) and z^(-2), you would use the double-wide previous states of y[n-1] and y[n-2]. just a recommendation i might make to make your life easier in the universe of fixed-point arithmetic. > Thanks FWIW. r b-j
From: Manny on 29 Jul 2010 17:49 On Jul 29, 8:47 pm, spop...(a)speedymail.org (Steve Pope) wrote: > Pete Fraser <pfra...(a)covad.net> wrote: > >I am working on a project where I need to > >implement 6-th order Butterworth low-pass > >filters in an FPGA. In some the bandwidth is > >low relative to the input data rate, whereas > >others have higher bandwidth. I can use ScopeIIR > >or Matlab to give me a good idea of coefficient > >accuracy for any given ratio of bandwidth to > >input sample rate. > > >However, I'm not sure what data-path accuracy > >I need (for 20-bit input / output accuracy). > >Is there a rule-of-thumb I can use, or do I just > >have to simulate the filter with real data and > >see what gives me low enough noise? > > You should simulate the fixed-point filter. When simulating, > you do not necessarily have to stimulate it with realistic data. I > often will stimulate the design being tested with bandlimited noise, and > measure the RMS error of output (relative to the same design, but in full > floating-point). Plotting the RMS error (in dBc) vs. RMS input level > gives you a very good idea of the dynamic range of the fixed point > design. > > >I was planning on using biquads, but I'm not sure > >whether I'm better off with DF1 or DF2 sections. > > You can do this, or you can use a lattice topology > (called "ARMA" in matlab/fdatool), which is the most > well-behaved topology. > > Steve I recently did just that and concurs with everything Steve said. Most important figure you need to keep track of is your I/O RMS with the various quantizations and casts you'd have applied. The places where casting occurs is of particular importance here and is structure- related. If your realization is sequential it'd be even harder to sort out. My final filter was DF2 with a shared biquad core and a memory trace for states and biquad inputs and outputs. The best performance for casting you get from convergent. Keep simulating various scenarios and look at your RMS and play with your structure, quantization, and castings until you land something satisfactory. Looking at my core's generics, here are what worked quite well for me: - core: rolled IIR DF2 SOS - sample word width: 16 - internal state width: 25 - internal fract width: 15 - coeff word width: 17 - coeff fract width: 15 - output scaling: YES Regards, -Momo
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