Matlab extensions for the development testing and verification of real time dsp software
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Matlab Extensions for the Development, Testing and Verification of Real-Time DSP Software. David P. Magee Communication Systems Engineer Texas Instruments Dallas, TX. Presentation Outline. DSP Software Development DSP Simulator Introduction to Intrinsics FFT Example

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Matlab extensions for the development testing and verification of real time dsp software

Matlab Extensions for the Development, Testing and Verification of Real-Time DSP Software

David P. Magee

Communication Systems Engineer

Texas Instruments

Dallas, TX


Presentation outline

Presentation Outline

  • DSP Software Development

  • DSP Simulator

  • Introduction to Intrinsics

  • FFT Example

  • Algorithm Optimization Results

  • Other Matlab and Simulink Extensions

  • Closing Remarks

  • Q & A


Dsp software development

Develop Floating

Point Simulation

Debug

Simulation

Step 1: Develop

Understanding

Develop Fixed

Point Simulation

Debug

Simulation

Step 2: Address

Scaling Issues

Develop

Assembly Code

Debug

Assembly Code

Step 3: Optimize

for Performance

DSP Software Development

  • Common steps for DSP software development


Issues with the 3 step approach

Issues with the 3 Step Approach

  • Each step takes time and resources

  • Algorithm testing at each stage

  • Multiple versions of the algorithm – version control headaches

  • Evaluation of processor instruction set compatibility and MIPS requirements often occurs late in the software development cycle

  • Debugging algorithms on a pipelined and/or parallel processor can be very difficult (the problem is getting more difficult as processors become more complicated)

    Can the development cycle be improved ?

Yes !


Improved software development cycle

Develop Floating

Point Simulation

Debug

Simulation

Step 1: Develop

Understanding

Simultaneously

Develop Fixed

Point Simulation

and Assembly

Code

Simultaneously

Debug

Simulation and

Assembly Code

Step 2: Address

Scaling Issues and

Optimize for

Performance

Improved Software Development Cycle

  • Merge Steps 2 and 3

Question: How can these steps be combined ?


Matlab dsp simulator

Floating Point

Simulation

System

Simulation

Matlab Simulation

Environment

Fixed Point

Simulation

System

Simulation

Host

Environment

DSP

Simulator

Matlab + DSP Simulator

  • Develop Floating Point and Fixed Point Simulations in a single development environment - Matlab

  • Develop and test C/C++ code for Fixed Point Simulation in cooperation with the DSP Simulator

  • Migrate the C/C++ code directly to the target DSP


Dsp simulator in matlab

DSP Simulator

C/C++ code

MEX-file

Matlab

DSP Simulator in Matlab

Develop and Debug Fixed Point

C/C++ Code in Matlab

Benefits:

  • Accelerate the development and analysis of DSP code

  • A mechanism to implement your IP blocks in efficient DSP code

  • Process large amounts of data

  • Compare fixed point and floating point algorithm implementations

  • Provide mixed simulation environment with fixed point and floating point algorithm implementations

  • Advanced graphing capabilities


What is a mex file

What is a MEX-file ?

  • A file containing one function that interfaces C/C++ code to the Matlab shell

  • MathWorks specifies the syntax for this function

    void mexFunction(int nlhs,mxArray *plhs[ ],

    int nrhs,const mxArray *prhs[ ])

  • See http://www.mathworks.com

    • Enter mex files into their Search engine


What is a dsp simulator

What is a DSP Simulator ?

  • A library of functions that simulate the mathematical operations of DSP assembly instructions.

  • For TI DSPs, the compiler recognizes special functions called Intrinsics and maps them directly into inline assembly instructions

  • In the DSP Simulator, make each function represent a supported compiler Intrinsic


Intrinsic example

C code

C6x Assembly Code

Function Example() {

.

y = _add2(a,b);

.

}

Example:

.

ADD2 . S1 A1,A2,A3

.

.

Intrinsic Example

  • ADD2: adds the upper and lower 16-bit portions of a 32 bit register

  • Intrinsic: dst = _add2(src1,src2)

  • Assembly Instruction: ADD2 (.unit) src1,src2,dst

Compile


Dsp simulator example

DSP Simulator

typedef struct _REG32X2

{

short lo;

short hi;

} reg32x2;

int32 _add2(int32 a,int32 b) {

int32 y;

reg32x2 *pa,*pb,*py;

pa = (reg32x2 *)&a; pb = (reg32x2 *)&b;

py = (reg32x2 *)&y;

py->lo = pa->lo+pb->lo;

py->hi = pa->hi+pb->hi;

return(y);

} // end of _add2() function

C code

Function Example() {

.

y = _add2(a,b);

.

}

DSP Simulator Example

  • C Code with _add2() Intrinsic


Dsp simulator

DSP Simulator

  • How many Intrinsics exist for each DSP family ?

TMS320C54x: 36

TMS320C55x: 42

TMS320C62x: 59

TMS320C64x: 135

TMS320C64+: 162

TMS320C67x: 68

Most algorithms previously written in assembly code can now be expressed in C/C++ code with Intrinsic function calls


Dsp simulator1

DSP Simulator

  • Consists of two files

    • C6xSimulator.c

    • C6xSimulator.h

  • Contains C functions for representing the numerical operations of 158 DSP assembly instructions

  • Can control endianness with a symbolic constant


Dsp simulator and c

DSP Simulator and C++

  • DSP Simulator works in C++ programming environments

    • Partition data into appropriate types (real, complex) and bit widths (8/16/32 bits)

    • Write functions in C++

    • Use operator overloading for required data types to map operators to the desired Intrinsic functions

Benefit: Operator overloading allows for easy migration to next generation DSP instruction sets


Using the dsp simulator

Using the DSP Simulator

  • Develop C/C++ code with Intrinsic function calls

  • Compile and link the C/C++ code and the DSP Simulator to form a Matlab executable file

  • Debug and evaluate the performance of the fixed point algorithms in Matlab

  • Rely on TI tools to generate an optimized assembly version of the C/C++ code for the target DSP

Benefit: One version of C/C++ code runs in Matlab and in the target DSP !


Migrating c c code to the dsp

Migrating C/C++ Code to the DSP

  • How does it work ?

C/C++ code can directly access DSP assembly instructions without actually writing assembly code

Benefit: Eliminate headaches associated with assembly programming

  • Pipeline scheduling

  • Register allocation

  • Unit allocation

  • Stack manipulation

  • Parallel instruction debug

Conclusion: Make the compiler do the hard work !


When is the c c code optimized

When is the C/C++ Code Optimized ?

  • Look at compiler report in the assembly file to determine unit loading.

    • Look at the assembly code. Are all the units being used each cycle ?

    • Try to balance loading by using different sequence of Intrinsics to perform the same overall mathematical operation.

      • e.g. X * 4 => X << 2

    • May require manual unrolling of loops.

  • Determine the ideal number of MAC operations for an algorithm and compare it to the compiler report


Limitations

Limitations

  • DSP software engineer must perform algorithm mapping from floating point to fixed point manually

    • ranges for floating point values

    • fixed point scaling issues

    • saturation issues

  • DSP software architecture is limited to the creativity of the software engineer

Recommendation: Develop an automated tool that converts Matlab/Simulink floating point files to fixed point DSP C/C++ code using the programming guidelines discussed in the paper.


Fft example

FFT Example

Developed an FFT for the C64x DSP architecture

Briefly discuss

  • FFT Functions

  • FFT Simulation File

  • Development time between hand coded assembly and C code with Intrinsics

    • Software development time

    • Software performance


Fft functions

// inside the Radix-2 stage

for(k=Nover2;k>0;k--)

{

.

// compute the real part

// (x0.real-x1.real)*w1.real

reg2 = _mpyhir(w1,reg1real);

// (x0.imag-x1.imag)*w1.imag

reg3 = _mpylir(w1,reg1imag);

reg2 -= reg3;

// compute the imag part

// (x0.imag-x1.imag)*w1.real

reg4 = _mpyhir(w1,reg1imag);

// (x0.real-x1.real)*w1.imag

reg5 = _mpylir(w1,reg1real);

reg4 += reg5;

.

}

FFT Functions

The FFT functions

  • Main FFT function

  • First FFT stage

  • Radix-2 stage

  • Radix-4 stage

  • Last FFT stage

    Example: Radix-2 stage

  • Uses mpyhir() and mpylir() Intrinsics

Note: Twiddle factor indexing not shown in this Example


Fft simulation file

% test_fft.m

% initialize some parameters

Nin = 64;

N = 128;

NumFFTs = 1000;

% create a random input

h = rand(NumFFTs,Nin);

h = [h;zeros(NumFFTs,N-Nin)];

% compute FFT using Matlab function

Hd = fft(h,[],2);

% call the fixed point function

[H] = ti_fft(h1dfilt,Nin,N);

% compute the NSR in dB scale

e = Hd-H;

NSR = 10*log10(sum(abs(e).^2,2)…

./sum(abs(Hd).^2,2));

FFT Simulation File

The simulation file is a Matlab script file

  • Performs the simulation

  • Calls the floating point Matlab FFT function fft()

  • Calls the fixed point FFT function ti_fft()

  • Compares the frequency responses of fixed point and floating point FFTs in Matlab

  • Computes the SNR, NSR, etc. using Matlab


Fft development time

FFT Development Time

Software Development Time Comparison

  • Time required to develop hand-coded assembly functions

    • 2-3 person months

  • Time required to develop C code with Intrinsic function calls

    • 2-3person weeks

Development time is reduced by a factor of 4 to 5 !


Fft performance comparison

FFT Performance Comparison

Metric: Kernel sizes and cycle counts

  • Kernel sizes for hand-coded assembly functions

    • FirstFFTStage:18*(N/16)

    • R2Stage:7*(N/8)

    • R4Stage:12*(N/8)

    • LastFFTStage:24*(N/16)

  • Kernel sizes for C code with Intrinsic function calls

    • FirstFFTStage:19*(N/16)

    • R2Stage:8*(N/8)

    • R4Stage:14*(N/8)

    • LastFFTStage:27*(N/16)

Intrinsics performance is within 15% of assembly !


Algorithm optimization results

Algorithm Optimization Results

In most cases, Intrinsics performance is within 10% !


Matlab function libraries

DSP Simulator

Library

Function N

Function 1

Function 2

C/C++ code

MEX-file

Matlab

Matlab Function Libraries

For a particular DSP application

  • The DSP Simulator emulates the numerical behavior of the DSP instructions

  • Power User develops a library of optimized algorithms that contain Intrinsic function calls

  • General user writes C/C++ code that calls the optimized functions in the library

  • The user’s C/C++ code is compiled with the DSP Simulator, the library and the MEX-file

  • User tests the algorithms for performance, evaluates cycle counts, etc. in Matlab

  • The same C/C++ code is migrated directly to the target DSP


Matlab function library examples

Library

Library

Library

NoiseEst

NoiseEst

ChanEst

ResEqu

SlidingMode

Hinf

OuterProduct

InnerProduct

PID

FIR

RS

BF

IC

VectorSum

Viterbi

Matlab Function Library Examples

Math Library

Communications Library

Controls Library

Benefit: Ability to share fixed-point DSP C/C++ code and test vectors between multiple users


Closing remarks

Closing Remarks

DSP Simulator Benefits

  • Develop fixed point DSP code in Matlab

  • Easily compare floating point and fixed point algorithm implementations in Matlab

  • Bit-true, fixed point simulations

  • Reduce software development time by a factor of 4 to 5

  • Incorporate DSP code into higher level system simulations

  • Debugging code in Matlab is easier than in a real-time system

  • Easily evaluate/predict MIPS requirements

  • Run the same C/C++ source code in Matlab and in the DSP

  • Easily migrate algorithms to new DSP instruction sets

  • Develop software before next generation DSPs are available


Matlab extensions for the development

Q & A

  • Thanks for attending my presentation !


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