design and verify embedded signal processing systems using...

1 © 2013 The MathWorks, Inc.

Design and Verify

Embedded Signal Processing Systems

Using MATLAB and Simulink

Giorgia Zucchelli, Application Engineer, MathWorks

10 January 2013, Technical University Eindhoven

2

Agenda

Introduction to Model Based Design

Application example: Parametric Audio Equalizer

– Targeting a fixed-point embedded processor

Workflow overview

Conclusions

3

INTRODUCTION

4

What is Model Based Design?

Methodology to design complex systems

– Using models and simulation

– Using tools for automation

5

Why using Model Based Design?

Find errors early

Reduce costly prototypes

Increase productivity

6

1. Focus on algorithmic design

2. Anticipate implementation

3. Verification test-benches

MathWorks model-based design

speeds up the development process

INTEGRATION

IMPLEMENTATION

TE

ST

& V

ER

IFIC

AT

ION

DESIGN

RESEARCH REQUIREMENTS

7

Improve team communication with

multi-domain executable specifications

Many trusted functions

Use the most suitable

modeling approach

INTEGRATION

IMPLEMENTATION

DESIGN

TE

ST

& V

ER

IFIC

AT

ION


Algorithms

8

Achieve early verification with refined models

anticipating real impairments

Bit-true simulation

Multi-domain physical

models

INTEGRATION

IMPLEMENTATION

DESIGN

TE

ST

& V

ER

IFIC

AT

ION


Fixed-Point

Physical Models

Algorithms

Analog Digital

9

Rapid prototyping with code generation:

less debugging, better design

C / C++

Synthesizable HDL

INTEGRATION

IMPLEMENTATION

DESIGN

TE

ST

& V

ER

IFIC

AT

ION


Fixed-Point

Physical Models

Algorithms

Analog Digital

FPGA ASIC

VHDL, Verilog

MCU DSP

C, C++

Processors

10

One testbench fits all:

unambiguous verification of the specs

INTEGRATION

IMPLEMENTATION

DESIGN

TE

ST

& V

ER

IFIC

AT

ION


Fixed-Point

Physical Models

Algorithms

Analog Digital System-level test

Co-simulation

“Hardware in the loop”

verification

FPGA ASIC

VHDL, Verilog

MCU DSP

C, C++

Processors

11

APPLICATION EXAMPLE

12

Demo: Parametric Audio Equalizer Digital filters used to adjust the frequency content of an audio signal

Parametric response that can be run-time controlled

Three band equalizer

– Low Band: 60 to 1500 Hz

– Mid Range: 1200 to 4800 Hz

– High Range: 4800 to 12 kHz

– Amplitude range: -8 to +8 dB

13

TEXAS INSTRUMENTS

DM6437 EVM

14

Target #1: TI DM6437 EVM

CAN/Serial Ethernet USB

JTAG

Processor

PCI

Video

Audio

PMU

15

DM6437 EVM - Processor

Highest-performance fixed-point DSP

generation in the TMS320C6000™ DSP

platform

Very-long-instruction-word (VLIW)

architecture developed by Texas

Instruments (TI)

Some of the specs:

– 2.5-, 2-, 1.67, 1.51-, 1.43-ns Instruction Cycle

Time

– 400-, 500-, 600-, 660-, 700-MHz C64x+™

Clock Rate

– Eight 32-Bit C64x+ Instructions/Cycle

– 3200, 4000, 4800, 5280, 5600 MIPS

16

DM6437 – Software stack

17

WORKFLOW OVERVIEW

18

Simulink - Host

PC-Based Audio Prototyping

INTEGRATION

IMPLEMENTATION

DESIGN

REQUIREMENTS

Fixed-Point

MCU DSP

C, C++

Processors

Algorithms

TE

ST

& V

ER

IFIC

AT

ION

Simulink / MATLAB

Data

source

Data

analysis

19

Simulink - Host

Fixed-Point Modeling

INTEGRATION

IMPLEMENTATION

DESIGN

REQUIREMENTS

Fixed-Point

MCU DSP

C, C++

Processors

Algorithms

TE

ST

& V

ER

IFIC

AT

ION

Fixed-Point

Simulink / MATLAB

Data

source

Data

analysis

20

Simulink - Host

C Code Verification

C code

INTEGRATION

IMPLEMENTATION

DESIGN

REQUIREMENTS

Fixed-Point

MCU DSP

C, C++

Processors

Algorithms

TE

ST

& V

ER

IFIC

AT

ION

Black Box

S-function

Data

analysis

Data

source

21

Simulink - Host

Automatic C Code Generation

Embedded

C

INTEGRATION

IMPLEMENTATION

DESIGN

REQUIREMENTS

Fixed-Point

MCU DSP

C, C++

Processors

Algorithms

TE

ST

& V

ER

IFIC

AT

ION

Fixed-Point

Simulink / MATLAB

Data

analysis

Data

source

22

Data

analysis

Data

source

Simulink - Host

Processor-in-the-Loop

?

Target

Embedded

C

INTEGRATION

IMPLEMENTATION

DESIGN

REQUIREMENTS

Fixed-Point

MCU DSP

C, C++

Processors

Algorithms

TE

ST

& V

ER

IFIC

AT

ION

Simulink / MATLAB

? ?

23

Simulink - Host

On-Target Rapid Prototyping

?

Target

DA

C

Embedded

C AD

C

INTEGRATION

IMPLEMENTATION

DESIGN

REQUIREMENTS

Fixed-Point

MCU DSP

C, C++

Processors

Algorithms

TE

ST

& V

ER

IFIC

AT

ION

Simulink / MATLAB

Data

analysis

Data

source

24

Fixed-point design: motivation

ASIC/FPGA or fixed-point DSP implementation

Saves power and/or cost

Word length and fraction length must be specified

sign + integer fractional

L

L-N N

25

Fixed-point design: challenges

It introduces degradation that must be assessed

– Quantization error

– Overflow / Underflow

All variables must be converted including internals: this

is error prone and often uninspiring


L

L-N N

Quantization overflow

26

Fixed-point data type in MATLAB / Simulink

A_fp = fi(A, 1, 32, 10)


L

L-N N

Quantization overflow

27

CONCLUSIONS

28

Quickly Iterate between Idea and Prototype

First prototype is functionally correct with automatic C

code generation

Spend your time in optimizing rather than debugging the

code

Find errors reusing the same testbench at each design

step

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