Single precision FFT written in ARM assembler

V0.52 6th Oct 2019
Author: Peter Hinch
Requires: ARM platform with FPU (e.g. Pyboard 1.x, Pyboard D). Any firmware version dated 2018 or later.

Contents

1. Overview
2. Design
   2.1 Future development
3. Getting Started
4. The DFT class
   4.1 Conversion types
   4.2 The populate function
   4.3 The window function
   4.4 FORWARD transform
   4.5 REVERSE transform
   4.6 POLAR transform
   4.7 DB transform
5. The DFTADC class
6. Implementation
7. Note for beginners
8. Performance
9. Whimsical observations

1. Overview

The DFT class is intended to perform a fast discrete fourier transform on an array of data typically received from a sensor. Apart from initialisation most of the code is written in ARM assembler for speed. It uses the floating point coprocessor and does not allocate heap storage: it can therefore be called from a MicroPython interrupt handler. See section 8 for benchmark results on the various Pyboard options.

The DFT is performed using the Cooley-Tukey algorithm. This requires arrays of length 2**N where N is an integer. The "twiddle factors" are precomputed in Python. The algorithm performs the conversion in place to minimise RAM usage (i.e. the results appear in the same arrays as the source data).

Inverse transforms and fast Cartesian to polar conversion are supported, as is the use of window functions. There is an option to convert polar magnitudes to dB.

The principal purpose of this library is for processing signals acquired from a Pyboard ADC. Features are targeted at typical engineering applications. It can be used with arbitrary data in other applications, but such users may also want to consider ulab which is a "micro" version of NumPy implemented as a C module.

2. Design

This code obsoletes my integer based converter which was written before the inline assembler supported floating point instructions: the ARM FPU is so fast that integer code offers no speed advantage. The use of floating point avoids problems with scaling and loss of precision which become apparent when integers are used for transforms with more than 256 bins.

adc.read_timed() may be used for data acquisition. It blocks until completion but is designed to work up to about a 750KHz sample rate.

Conversion from Cartesian to polar is performed in assembler using an approximation to the math.atan2() function. Its accuracy is of the order of +-0.085 degrees.

Calculations use single precision floating point on all platforms.

2.1 Future development

Modern compilers mean that the traditional performance benefit of assembler is nonexistent unless the assembler code is hand crafted and optimised to an extreme level. This is because the compiler exploits details of the internal design of the CPU in ways which are difficult for the programmer to achieve.

The benefit of the inline assembler (compared to C modules) is that the code may be run on a standard firmware build. When dynamically loaded C modules arrive this will no longer apply. The drawback of assembler is that it is not portable.

I may rewrite this library as a dynamically loadable C module.

3. Getting Started

The first step is to determine how to populate the real data array. If you are using the Pyboard and intend to use an onboard ADC, one option is to use the ADC's read_timed() method. This can acquire data at high speed but has the drawack of blocking for the duration of the read. Its use is supported by the DFTADC class:

from dftclass import DFTADC, POLAR
mydft = DFTADC(128, "X7")
 # Acquire data for 0.1 secs and convert
mydft.run(POLAR, 0.1)  # values are in mydft.re and mydft.im

Where the data is to be acquired by other means you will need to instantiate a DFT object and provide a function to populate its re array. For synthetic data this is straightforward. Data from sensors is usually in the form of integers which will need to be converted to floats. While this is trivial in Python, if speed is critical the window.icopy function can copy and convert an integer array to one of floats (see the DFTADC class). The test programs dftadc.py, dftadc_tests.py and dfttest.py provide examples, the latter showing the use of synthetic data.

Test programs dftadc.py and dfttest.py provide means of demonstrating the code with ADC and synthetic data respectively. dftadc_tests.py also illustrates the use of a window function. For ease of reading the test programs print phase angles in degrees.

Test programs require dft.py, dftclass.py, polar.py, and window.py. Note that dft.py cannot be frozen as bytecode because of it use of assembler.

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4. The DFT class

This is the interface to the conversion. The constructor takes the following arguments:

1. length Mandatory. Integer. The conversion length. Must be an integer power of 2.
2. popfunc=None An optional function to populate the real array.
3. winfunc=None An optional window function.

Method:

• run Mandatory arg: conversion. Specifies the conversion type. See below. Returns the time in μs taken by the raw conversion.

Properties:

• scale Integer. Read/write. The consructor initialises this with the default scaling factor 1/length. This may be modified prior to executing run().
• length Integer. Read only. The transform length.

User-accessible bound variables:

• re Real data array. Elements are of type float.
• im Imaginary data array. Elements are of type float.
• dboffset Float. Offset for dB conversion. Default 0. See section 4.7.

4.1 Conversion types

These constants in dftclass.py are passed to DFT.run() and define the conversion to be performed. The following are the options, described in detail below:

4.2 The populate function

This optional function is called each time run is executed. Its purpose is to populate the re data array, possibly by accessing hardware. It receives the DFT instance as its arg. Any return value is ignored. Any windowing is applied after it returns.

4.3 The window function

A discussion of the purpose of window functions is outside the scope of this document. See:
Mathematical background
Engineer's guide

This optional function takes two arguments:

• x Point number (0 <= number < length).
• length Transform length.

It should return the window function value for the specified point. Commonly this is in range 0-1.0. A typical window function is the Hanning (Hann) function:

def hanning(x, length):
    return 0.5 - 0.5*math.cos(2*math.pi*x/(length-1))

This has a -6dB coherent gain which may be offset by multiplying by 2 to preserve signal amplitude:

def hanning(x, length):
    return 1 - math.cos(2*math.pi*x/(length-1))

4.4 FORWARD transform

Forward transforms assume real data: you only need to populate the real array. The imaginary array is zeroed by DFT.run() before a conversion is performed. By default values are scaled by the transform length to produce mathematically correct values. The forward() function in dfttest.py provides an example of this.

The result comprises complex data in the DFT object's re and im arrays.

4.5 REVERSE transform

These accept complex data in the DFT object's re and im arrays. If you use a populate() function it must initialise both arrays. The trev() function in dfttest.py provides an example of this.

The conversion result comprises complex data in the DFT object's re and im arrays.

4.6 POLAR transform

This is a forward transform with results converted to polar coordinates.

On completion the magnitude is in the DFT object's re array and the phase is in im. Phase is in radians with the same conventions as math.atan2(). The test() function in dfttest.py provides an example of this.

For performance only the first half of re and im arrays are converted. The complex conjugates are ignored.

4.7 DB transform

This is a forward transform with results converted to polar coordinates. The magnitude is converted to dB. The 0dB level defaults to 1VRMS. Magnitudes are scaled by subtracting the dboffset bound variable. Magnitudes <= 0.0 are returned as -80dB. The dbtest() function in dfttest.py provides an example of this.

On completion the magnitude is in the DFT object's re array and the phase is in im. Phase is in radians in a form compatible with math.atan2().

For performance only the first half of re and im arrays are converted. The complex conjugates are ignored.

As noted above the 0dB reference voltage is determined by the bound variable DFTADC.dboffset. An explanation of the calculation of its value may be found in comments in dftclass.py. The value may be changed prior to performing a DB transform to change the reference voltage.

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5 The DFTADC class

This supports input from a Pyboard ADC using pyb.Timer.read_timed. Its base class is DFT.

Costructor. This takes the following args:

1. length Mandatory. Integer defining transform length.
2. adcpin Mandatory. This may take an ADC instance or an object capable of defining one e.g. 'X7' or pyb.Pin.board.X19.
3. winfunc=None Window function. See section 4.3.
4. timer=6 Can take a pyb.Timer instance or a timer no. Defines the timer used for data acquisition.

The constructor sets the dboffset bound variable so that the scaling is such that 0dB corresponds to a 1V RMS sinewave applied to the Pyboard ADC (with suitable DC bias). This only affects DB conversions.

Method.

• run Mandatory args: conversion, duration. Returns the time in μs taken by the conversion from the time of completion of data acquisition to the completion of conversion.

conversion must be one of the forward conversion types defined in section 4.1.
duration Integer or float. Acquisition duration in seconds.

run will block for the duration.

In the case of DB conversions scaling may be modified by altering the dboffset bound variable.

6. Implementation

The DFT constructor creates and initialises three member float arrays, re, im, and cmplx and an integer array ctrl. The first two store the real and imaginary parts of the input and output data: for a 256 bin transform each will use 1KB of RAM. The ctrl and cmplx arrays are small (total size of the order of 120 bytes, size of the latter varies slightly with transform length) and contains data used by the transform itself, including a one-off calculation of the roots of unity (twiddle factors). There is no need to access this data, but for those wishing to understand the code the structure of the ctrl and cmplx arrays is documented in ctrlmap.ods.

The constructor is pure Python as it is assumed that the speed of initialisation is not critical. The run() member function which performs the transform uses assembler for iterative routines in an attempt to optimise performance. The one exception is dB conversion of the result which is in Python.

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7. Note for beginners

This README does assume some familiarity with sampling theory and the DFT. It is worth noting that, in any sampled data system, precautions need to be taken to prevent a phenomenon known as aliasing. If you read the ADC at 1mS intervals, the maximum frequency which can be extracted from the set of samples is 500Hz. If signals above this frequency are present in the input analog signal, these will incorrectly appear as signals below this frequency. This is a fundamental property of all sampled data systems and you need to ensure that such signals are removed. Typically this is performed by a combination of analog and digital filtering.

Assume you read the ADC over one second and do a 1024 point DFT. Samples will be acquired at a rate of 1.024KHz. However as described above the maximum frequency which can be acquired at that rate is 512Hz. The output data from the conversion occupies 1024 frequency "bins". bin[0] contains the DC component. bin[1] contains the 1Hz component up to bin[511] with 511Hz.

Bins from 1023 downwards contain complex conjugates of the lower bins, so bin[1023] contains 1Hz conjugate, bin[1022] 2Hz and so on. Frequencies are represented as two contra-rotating phasors with the same magnitude but opposite phase (complex conjugates) which add to produce a real voltage.

As such these higher bins contain no information and can be ignored: simply double the real part of the lower order bins to retrieve the voltage.

8. Performance

The script dftbench.py times a 1024 point forward transform in a way that mimics a typical application. In such an application the DFTADC would be instantiated at the start. Data would be acquired repeatedly from an ADC at an application dependent rate. Critical timing is from the end of data acquisition to the availability of transform data. This is the interval that dftbench measures. Results were:

9. Whimsical observations

At one time a 1024 point DFT was widely used as a computer benchmark. As such they were implemented in highly optimised assembler. I can't make this claim: my code could be significantly improved. It does it in 12.9mS on a Pyboard. It costs £28.

One of the first supercomputers, a Cray 1, took 9mS. It cost a king's ransom.

My own introduction to DFT involved punching cards, handing them in to the computer operator, and retrieving a listing (often with only an error code) the following day...

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