Lecture 7 Real-time Digital Signal Processing with the TMS320C6x Dr. Konstantinos Tatas.

Lecture 7

Real-time Digital Signal Processing with the TMS320C6x

Dr. Konstantinos Tatas

Outline• Digital Signal Processing Basics

– ADC and sampling– Aliasing– Discrete-time signals– Digital Filtering– FFT– Effects of finite fixed-point wordlength

• Efficient DSP application implementation on TMS320C6x processors– FIR filter implementation– IIR filter implementation

Digital Signal Processing Basics• A basic DSP system is composed of:• An ADC providing digital samples of an analog input• A Digital Processing system (μP/ASIC/FPGA)• A DAC converting processed samples to analog output• Real-time signal processing: All processing operation

must be complete between two consecutive samples

ADC and Sampling

• An ADC performs the following:– Sampling– Quantization– Binary Coding

• Sampling rate must be at least twice as much as the highest frequency component of the analog input signal

Aliasing• When sampling at a rate of fs samples/s, if k is any

positive or negative integer, it’s impossible to distinguish between the sampled values of a sinewave of f0 Hz and a sinewave of (f0+kfs) Hz.

Discrete-time signals

• A continuous signal input is denoted x(t)

• A discrete-time signal is denoted x(n), where n = 0, 1, 2, …

• Therefore a discrete time signal is just a collection of samples obtained at regular intervals (sampling frequency)

Common Digital Sequences

• Unit-step sequence:

• Unit-impulse sequence:

0,0

0,1)(

n

nnu

0,0

0,1)(

n

nn

1

0 1 2 3 4

. . .

n

u(n)

-1-2

1

0 1 2 3 4

. . .

n

δ(n)

-1-2

The z transform

• Discrete equivalent of the Laplace transform

z-transform properties

• Linear

• Shift theorem

• Note:

))(()(())()(( nybZnxaZnbynaxZ

mznxZmnxZ ))(())((

)())(( zXnxZ

Transfer Function

• z-transform of the output/z transfer of the input

• Pole-zero form

)(

)()(

zX

zYzH

Pole-zero plot

System Stability• Position of the poles affects system stability• The position of zeroes does not

Example 1

• A system is described by the following equation:

• y(n)=0.5x(n) + 0.2x(n-1) + 0.1y(n-1)

• Plot the system’s transfer function on the z plane

• Is the system stable?

• Plot the system’s unit step response

• Plot the system’s unit impulse response

The Discrete Fourier Transform (DFT)

• Discrete equivalent of the continuous Fourier Transform

• A mathematical procedure used to determine the harmonic, or frequency, content of a discrete signal sequence

•

The Fast Fourier Transform (FFT)

• FFT is not an approximation of the DFT, it gives precisely the same result

Digital Filtering• In signal processing, the function of a filter is to remove

unwanted parts of the signal, such as random noise, or to extract useful parts of the signal, such as the components lying within a certain frequency range

• Analog Filter:– Input: electrical voltage or current which is the direct analogue of

a physical quantity (sensor output)– Components: resistors, capacitors and op amps– Output: Filtered electrical voltage or current – Applications: noise reduction, video signal enhancement,

graphic equalisers• Digital Filter:

– Input: Digitized samples of analog input (requires ADC)– Components: Digital processor (PC/DSP/ASIC/FPGA)– Output: Filtered samples (requires DAC)– Applications: noise reduction, video signal enhancement,

graphic equalisers

Averaging Filter

Ideal Filter Frequency Response

Realistic vs. Ideal Filter Response

FIR filtering

• Finite Impulse Response (FIR) filters use past input samples only

• Example:• y(n)=0.1x(n)+0.25x(n-1)+0.2x(n-2)• Z-transform: Y(z)=0.1X(z)+0.25X(z)z^(-

1)+0.2X(z)(z^-2)• Transfer function:

H(z)=Y(z)/X(z)=0.1+0.25z^(-1)+0.2(z^-2)• No poles, just zeroes. FIR is stable

FIR filter design

• Inverse DFT of H(m)

FIR Filter Implementation

• y(n)=h(0)x(n)+h(1)x(n-1)+h(2)x(n-2)+h(3)x(n-3)

Example 2

• A filter is described by the following equation:• y(n)=0.5x(n) + 1x(n-1) + 0.5x(n-2), with initial

condition y(-1) = 0• What kind of filter is it?• Plot the filter’s transfer function on the z plane• Is the filter stable?• Plot the filter’s unit step response• Plot the filter’s unit impulse response

IIR Filtering

• Infinite Impulse Response (IIR) filters use past outputs together with past inputs

IIR Filter Implementation• y(n)=b(0)x(n)+b(1)x(n-1)+b(2)x(n-2)+b(3)x(n-3) +

a(0)y(n)+a(1)y(n-1)+a(2)y(n-2)+a(3)y(n-3)

FIR - IIR filter comparison

• FIR– Simpler to design– Inherently stable– Can be designed to have linear phase– Require lower bit precision

• IIR– Need less taps (memory, multiplications)– Can simulate analog filters

Example 3

• A filter is described by the following equation:• y(n)=0.5x(n) + 0.2x(n-1) + 0.5y(n-1) + 0.2y(n-2),

with initial condition y(-1)=y(-2) = 0• What kind of filter is it?• Plot the filter’s transfer function on the z plane• Is the filter stable?• Plot the filter’s unit step response• Plot the filter’s unit impulse response

Software and Hardware Implementation of FIR filters

Fixed-Point Binary Representation

• Representation of a number with integer and fractional part:

• This is denoted as Qnm representation• The binary point is implied• It will affect the accuracy (dynamic range and

precision) of the number• Purely a programmer’s convention and has no

relationship with the hardware.

221021 ).......( knn dddddd

1

2n

mii rdN

Examples

• x = 0100 1000 0001 1000b• Q0.15 => x= 2^(-1) + 2^(-4) + 2^(-11)+2^(-12)• Q1.14 => x= 2^0 + 2^(−3) + 2^(−10) + 2^(−11)• Q2.13 => x = 2^1 + 2^(−2) + 2^(−9) + 2^(−10)• Q7.8 => x = ?• Q12.3 => x = ?

EFFECTS OF FINITE FIXED-POINT BINARY WORD LENGTH

• Quantization Errors– ADC– Coefficients

• Truncation

• Rounding

• Data Overflow

ADC Quantization Error

• ADC converts an analog signal x(t) into a digital signal x(n), through sampling, quantization and encoding

• Assuming x(n) is interpreted as the Q15 fractional number such that −1 ≤ x(n) < 1

• dynamic range of fractional numbers is 2. Since the quantizer employs B bits, the number of quantization levels available is 2B

• The spacing between two successive quantization levels is Δ = 2/2^B = 2^(1-B)

• Therefore the quantization error is |e(n)|≤Δ/2

Coefficient Quantization Error

• Effects on FIR filters– Location of zeroes changes– Therefore, frequency response changes

• Effects on IIR filters– Location of poles and zeroes change– Could move poles outside of unit circle,

leading to unstable implementations

Roundoff error

Overflow error

• signals and coefficients normalized in the range of −1 to 1 for fixed-point arithmetic, the sum of two B-bit numbers may fall outside the range of −1 to 1.

• Severely distorts the signal• Overflow handling

– Saturation arithmetic• “Clips” the signal, although better than overflow• Should only be used to guarantee no overflow, but should

not be the only solution

– Scaling of signals and coefficients

Coefficient representation

• Fractional 2’s complement (Q) representation is used

• To avoid overflow, often scaling down by a power of two factor (S) (right shift) is used.

• The scaling factor is given by the equation: S=Imax(|h(0)|+|h(1)|+|h(2)|+…)

• Furthermore, filter coefficient larger than 1, cause overflow and are scaled down further by a factor B, in order to be less than 1

Example 1

• Given the FIR filter– y(n)=0.1x(n)+0.25x(n-1)+0.2x(n-2)– Assuming the input range occupies ¼ of the full range– Develop the DSP implementation equations in Q-15

format. What is the coefficient quantization error?

• Solution:– S=1/4((|h(0)|+|h(1)|+|h(2)|) = ¼(0.1+0.25+0.2)=3.25/4– Overflow cannot occur, no input (S) scaling required– No coefficents > 1, no coefficient (B) scaling required

Example 2

• Given the FIR filter– y(n)=0.8x(n)+3x(n-1)+0.6x(n-2)– Assuming the input range occupies ¼ of the full range– Develop the DSP implementation equations in Q-15

format. What is the coefficient quantization error?

• Solution:– S=1/4((|h(0)|+|h(1)|+|h(2)|) = ¼(0.8+3+0.6)=4.4/4 =

1.1– Therefore: S=2– Largest coefficient: h(1) = 3, therefore B=4– ys(n)=0.2xs(n)+0.75xs(n-1)+0.15xs(n-2)

Simple FIR Hardware implementation

• VHDL 4-tap filter entityENTITY my_fir is

Port (clk, rst: in std_logic;

sample_in: in std_logic_vector(length-1 downto 0);

sample_out: out std_logic_vector(length-1 downto 0)

);

END ENTITY my_fir;

Example (continued)• VHDL 4-tap filter architectureARCHITECTURE rtl of my_fir istype taps is array 0 to 3 of std_logic_vector(length-1 downto 0); Signal h: taps_type;Signal x_p1, x_p2, x_p3: std_logic_vector(length-1 downto 0); --past samplesSignal y: std_logic_vector(2*length-1 downto 0); BeginProcess (clk, rst) BeginIf rst=‘1’ then

x_p1 <= (others => ‘0’);x_p2 <= (others => ‘0’);x_p3 <= (others => ‘0’);

Elsif rising_edge(clk) then x_p1 <= sample_in; --delay registers x_p2 <= x_p1; x_p3 <= x_p2; End if;End process;

y <= sample_in*h(0)+x_p1*h(1)+x_p2*h(2)+x_p3*h(3); Sample_out <= y(2*length-1 downto length);End;

FIR Software Implementationint yn=0; //filter output initializationshort xdly[N+1]; //input delay samples array

interrupt void c_int11() //ISR{

short i;yn=0;short h[N] = { //coefficients };xdly[0]=input_sample();for (i=0; i<N; i++)

yn += (h[i]*xdly[i]);for (i=N-1; i>0; i--)

xdly[i] = xdly[i-1]; output_sample(yn >> 15); //filter output

return; //return from ISR}

IIR C implementationint yn=0; //filter output initializationshort xdly[N+1]; //input delay samples arrayShort ydly[M]; //output delay array

interrupt void c_int11() //ISR{

short i;yn=0;short a[N] = { //coefficients };short b[M] = { //coefficients };xdly[0]=input_sample();for (i=0; i<N; i++)yn += (a[i]*xdly[i]);for (i=0; i<M; i++) yn += (b[i]*ydly[i]);for (i=N-1; i>0; i--)xdly[i] = xdly[i-1];

ydly[0] = yn >> 15;for (i=M-1; i>0; i--)ydly[i] = ydly[i-1];

output_sample(yn >> 15); //filter outputreturn; //return from ISR}