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I assume that fixed-point arithmetic can handle most of the traditional linear DSP tasks. As far as I know, there is a restriction for the FFT length with respect to the fixed point bit-depth.

Is there any common knowledge regarding other DSP applications that the fixed point can not handle? I assume that non-linear applications such as kurtosis might become sensitive to the rounding error but I am trying to gether a more solid knowledge of this topic.

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I don't think that there are any 'DSP applications that the fixed point can not handle.' Digital designers carefully take the accuracy and range of fixed-point numbers into consideration.

Fixed-point numbers are continuously scaled up to avoid bit loss, and down to avoid bit overflow. This happens after every single mathematic operation to maximize the number of bits in use. During this time, the designer keeps track of the scaling up and down. It is a very meticulous, thoughtful, and intentional process.

At the end of the algorithm, whatever remaining scaling hasn't been corrected, the designer can either perform one scaling operation, or put the results in block notation.

Fixed-point is very powerful. It can lead to greater resource effeciency and can even decrease total error. However, it requires much more careful planning and design than floating-point. Which can be slow and costly for a project.

This is why most companies break the job into two positions: algorithm development and digital design. Then there are people like myself who live in the middle between the two.

In conclusion, I don't think your assumption is correct. Not in my experience, at least. And I have worked on some very complex algorithms on very resource restricted platforms.

Hope this helps! Let me know if I can clarify anything.

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  • $\begingroup$ you're right as long as what you only want to ensure is that the dynamic range of a signal being preserved. However, if you want to transport the scale whilst still preserving the dynamic range, you'd have to lug around a fixed-point number and the factor by which e.g. the AGC scaled the receive signal. So, that's pretty much the description of a floating point number! So, yes, floating point numbers can do more with the same number of bits – for the right problem class. $\endgroup$
    – Marcus Müller
    Oct 24, 2022 at 18:38
  • $\begingroup$ Hi, Marcus. Perhaps I was not clear in my answer. I did not mean to covey that scaling factors are stored. It would be something hard-coded into the design. A very simple example of this is if you know that two unsigned numbers are being multiplied, you can bit-shift left by 1. This gains one bit of accuracy but distorts the final result by a factor of 2. At the end of the algorithm you could shift right by one to restore scaling. $\endgroup$
    – user58975
    Oct 24, 2022 at 21:40
  • $\begingroup$ Now when it comes to operations such as accumulation (like a single-term of the DFT), the bits grow at a log scale. This information can be used to increase accuracy while preserving bit a fixed number of bits. Then the final result can now be shifted back using this log factor. $\endgroup$
    – user58975
    Oct 24, 2022 at 21:45
  • $\begingroup$ Yes, but if you have signal that might span a large range, then having all of the bits to represent both the strongest as well as the weakest with sufficient signal-to-quantization noise-ratio takes very many bits in fixed point, and few in floating point. $\endgroup$
    – Marcus Müller
    Oct 24, 2022 at 21:46
  • $\begingroup$ While that is technically true, I always assume the signal is normalized by the AGC, and I never keep the AGC scaling factor because I'm more interested in its relative strength than its true strength. Even if the AGC scaling factor was kept, it could just be carried to the final result. I suppose that my point here is that fixed-point doesn't necessarily have these hard limits. It just requires a much more thoughtful design. $\endgroup$
    – user58975
    Oct 24, 2022 at 22:03
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There are two things to consider here: Signal To Noise (SNR) ratio and Dynamic range. Floating point offers a constant signal to noise ratio over a very wide dynamic range. Fixed point has a very limited dynamic range and the SNR is a direct funtion of the signal level itself.

Fixed point is problematic wherever SNR and dynamic range are tricky to manage. A good linear example are IIR filters especially with poles close to the unit circle (which is very common in Audio for example).

Fixed point IIR filters require very careful management of second order sections (pole/zero pairing, order, gain staging, section topology, etc), coefficient quantization, rounding strategies to minimize noise but avoid limit cycles, clipping prevention, headroom, etc.

For that reason there are some "hybrid" algorithms and data formats, that are somewhere between fixed point and full floating point and can be optimized for a specific application.

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  • $\begingroup$ Since you have given it as an example, seems that it was solved using Biquad Cascade IIR Filters - Direct Form I. I am troubled to understand the other scenarios, are all the DSP cards that do not support floating-point assume that I will implement an in-house floating-point type? $\endgroup$
    – Gideon Genadi Kogan
    Oct 25, 2022 at 7:26
  • $\begingroup$ Of course you use cascaded second order section in either Direct Form I or transposed form II but there is way more to be sorted out. You need to prevent each section from clipping and manage noise accumulation which both depend on section ordering. There is also limit cycle prevention, noise shaping, etc. If you can live with regular fixed point, you use that. If you can't, you can use double precision states, block floating point, etc to make it work. $\endgroup$
    – Hilmar
    Oct 25, 2022 at 12:54

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