Ultra sound solution Profiles and other optimizations

Ultra sound solution

Profiles and other optimizations

Research Team discussion -- RECAP

Ultra-sound probe (20 MHz) that sends out signals into body that reflect off moving blood cells in (Artery? Vein?)

Ultra-sound frequency received is Doppler shifted compared to transmitted frequency Same as sound when ambulance goes by. Higher

if approaching, lower if receding They get the positive frequencies (towards)

on the left audio channel and negative frequencies (away) on the right audio channel.

04/20/23.ENCM515 – Ultrasound ProblemCopyright [email protected] 2 / 33

Picture looks like this -- RECAP

Note that the display loses all direction information Can I help them to output the maximum frequency?

04/20/23ENCM515 – Ultrasound ProblemCopyright [email protected] 3 / 33

Progress to this stage -- RECAP

Gather single input values into buffer as part of ISR

When buffer ready, launch an TTCOS processing task that executes outside the ISR -- MUST COMPLETE in time N * deltaT where N is buffer size and deltaT is sample time

Take values from a previously processed buffer and send to output inside ISR

Explored a number of pragmas for optimizing C++ code and looked at impact of C code

Only looked at simple copy program


Snap shot code execution -- RECAP


A/D DATA CAPTURE INSIDE ISR

D/A DATA OPERATION INSIDE ISR

DATA PROCESSING OUTSIDE ISR

MAJOR PROBLEM -- NEED TO RETRIEVE FROM END AND BEGINNING DATA BLOCKAssignment 2 – FIR operations on BLOCKS is another example

Approaches – two common -- RECAP

Store complex number (a + jb) data block in two sections Real part in dm memory space and imaginary

part in pm memory space Where to store FIR coefficients to avoid data

data conflicts? Store complex number data block in dm

space in double size array float normalArray[LENGTH] float complexArray[2 *LENGTH] Perhaps store FIR coefficients in pm space


Proposed algorithm – based on STFT – Short term FT -- RECAP

Gather N points of data to circular buffer Using last 2 N points, and do on both channels

Convert 2N real points to 2N complex points Perform 2N point FFT (complex numbers) with

windowing Calculate absolute value of FFT of 2N points Calculate area under frequency curve (area) Find index where X% of frequencies are below this

frequency (max frequency) This one frequency value is used as the best

estimate of the maximum frequency over the last N points (put same value into all N points of output) (STFT limitation)


Switch to Testing framework -- RECAP

Don’t test with TTCOS and real audio signals Don’t know what expected values are Testing framework can’t work with TTCOS anyway – CHECK

errors take 1/ 30 s to print = lost samples 1500 Check circular buffer operation for data storage Check circular buffer operation when convert 2N real points to

2N complex points (different buffer, not circular buffer operations)

Check Absolute calculation (complex to real) Check area and frequency maximum calculations Check FFT. Turn on optimizer and ‘hope things work in real time’

Plan B – Find better FFT code, that compiler can more easily optimize (for loops)

Plan C -- SHARC has FFT Accelerator and there is sample code available


Revised code -- Error message strange


Errors and Defects -- RECAP We all make mistakes – the key is when we

find the mistake If we find the mistake AFTER we move away

from this part of the problem – we call the mistake – ‘An error’. Errors can be made in coding or design or life.

If we find the mistake AFTER we move away from this part of the problem – we call the mistake – ‘A defect’. Defects can be made in coding or design or life. Defects are more costly to correct that errors


Code for absolute of complex number



FFT code – legacy code Typical problem with legacy code

Written by somebody else – how good or bad?

No tests available Can be very difficult to write tests

Useful article and book www.objectmentor.com/resources/artic

les/WorkingEffectivelyWithLegacyCode.pdf

Michael Feathers04/20/23

ENCM515 – Ultrasound ProblemCopyright [email protected] 13 / 33

Some obvious tests for FFT Take any signal signal = IFFT(FFT(signal)) and signal = FFT(IFFT(signal))

WARNING signal starts as a real value and ends up as a complex value of the form

value + j (close but not equal to zero)


Obvious tests for FFT Based on linear algebra If input is size N and is all ones (DC)

then FFT results should have spike at frequency 0 with a size of N

If input is size N and signal is sin(2 * pi * f * t / N) then should have spike at frequency f with a size of N (actually two spikes one at f and the other at –f since sin = (exp(jwt) + exp(-jwt)) / 2j

Compare to MathLab results


Need a new RealToComplex( ) Note – framework indicates memory

leak


delete complexDCSignalat end of TEST

First set of FFT tests Common error S = IFFT( FFT(S) ) / N;

Matlab does this automatically – we must do manually TIME PENALTY


Make sure you rerun tests For every 3 errors fixed, 1 introduced

SUSPECTCODE


KNOWN DEFECT This should work with this new buffer and

old buffer, but uses next buffer instead

Difficult to spot, even after release, as all the buffers will be very similar so results will be “Almost right” – problem with medical device – especially if you are the patient


Switch on the optimizing compiler

DOES NOT HELP

Where’s the timing info gone? Have to go in and tell optimizing compiler

to add debug info and do rebuild


Here is the only compiler warning

If more info available could go to SIMD


2 / 2efficiency100%

If’s take5 / 7 of time


Detail – out of order execution


Performance hog – function in loop

Also software loop


3 cycles every time if not true

NOT PREDICTED


Compare code

BEFORE AFTER – SMALLER LOOP, NOT JUMP


Efficiency of FFT code Don’t even want to look Software bit reversing -- 30% of time

SHARC can for hardware bit reverse address

Continually calculates sine and cosine coeffs Okay if FFT done once – otherwise get an

algorithm that uses table lookup Those while loops


Next step Do we need to worry? Is real-time

performance there

Add tested code into TTCOS and do a quick test

TTCOS will send out error message if not working fast enough

Look at using profiler to find out where the code is the slowest


Add to TTCOS Ausio program


It links in Debug – will it run?


It runs TTCOS is not complaining “no room for

new tasks We are processing 128 points at 41000 Hz So each task must finish in 128 / 41000 s

Or around 1/ 500 s or 2 ms Or 2000 * 500 cycles at 500 MHz= 1,000,000

Every cycle can do many things

Lets turn profiling on


Turn on statistical profile What is a profiler? What is a statistical profiler?


Profile information – TOTALLY IMPRESSED

Actually works better than I expected (profiler and code) – If the code works

DEBUG OPTIMIZED


Get line by line information


Project Need to add the amplitude modulating

code Do some more testing

Use audacity as a simple oscilloscope Fix the known defects

Must use only ½ the FFT data and not all Using wrong ‘old buffer’

But I am impressed!! Project is essentially there in about 1 week

elapsed time



Documents

Ultra sound solution Profiles and other optimizations