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

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

David P. Magee

Communication Systems Engineer

Texas Instruments

Dallas, TX

Presentation Outline Verification of Real-Time DSP Software

- DSP Software Development
- DSP Simulator
- Introduction to Intrinsics
- FFT Example
- Algorithm Optimization Results
- Other Matlab and Simulink Extensions
- Closing Remarks
- Q & A

Develop Floating Verification of Real-Time DSP Software

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 Verification of Real-Time DSP Software

- 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 !

Develop Floating Verification of Real-Time DSP Software

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 ?

Floating Point Verification of Real-Time DSP Software

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 Verification of Real-Time DSP Software

C/C++ code

MEX-file

Matlab

DSP Simulator in MatlabDevelop 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 ? Verification of Real-Time DSP Software

- 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 ? Verification of Real-Time DSP Software

- 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

C code Verification of Real-Time DSP Software

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 Verification of Real-Time DSP Software

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 Verification of Real-Time DSP Software

- 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 Simulator Verification of Real-Time DSP Software

- 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++ Verification of Real-Time DSP Software

- 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 Verification of Real-Time DSP Software

- 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 Verification of Real-Time DSP Software

- 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 ? Verification of Real-Time DSP Software

- 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 Verification of Real-Time DSP Software

- 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 Verification of Real-Time DSP Software

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

// inside the Radix-2 stage Verification of Real-Time DSP Software

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 FunctionsThe 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

% test_fft.m Verification of Real-Time DSP Software

% 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 FileThe 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 Verification of Real-Time DSP Software

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 Verification of Real-Time DSP Software

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 Verification of Real-Time DSP Software

In most cases, Intrinsics performance is within 10% !

DSP Simulator Verification of Real-Time DSP Software

Library

Function N

Function 1

Function 2

C/C++ code

MEX-file

Matlab

Matlab Function LibrariesFor 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

Library Verification of Real-Time DSP Software

Library

Library

NoiseEst

NoiseEst

ChanEst

ResEqu

SlidingMode

Hinf

OuterProduct

InnerProduct

PID

FIR

RS

BF

IC

VectorSum

Viterbi

Matlab Function Library ExamplesMath Library

Communications Library

Controls Library

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

Closing Remarks Verification of Real-Time DSP Software

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

Q & A Verification of Real-Time DSP Software

- Thanks for attending my presentation !

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