1. Microcomputer Systems 1
Digital Systems: Hardware Organization and Design
11/19/2018
Microcomputer Systems 1
Introduction to DSP’s
Architecture of a Respresentative 32 Bit Processor

2. Digital Systems: Hardware Organization and Design
11/19/2018
Introduction to DSP’s
Definition:
DSP – Digital Signal Processing/Processor
It refers to:
Theoretical signal processing by digital means (subject of ECE3222, ECE3541),
Specialized hardware (processor) that can process signals in real-time (subject of this course ECE3551&2)
This class’s focus is on:
Hardware Architecture of a real-world DSP platforms
Software Development on DSPs, and
Applied Signal Processing theory.
19 November 2018
Veton Këpuska
Architecture of a Respresentative 32 Bit Processor

3. Digital Systems: Hardware Organization and Design
11/19/2018
Introduction to DSP’s
DSP’s process signals
Signal – a detectable physical quantity or impulse (as a voltage, current, or magnetic field strength) by which messages or information can be transmitted (Webster Dictionary)
19 November 2018
Veton Këpuska
Architecture of a Respresentative 32 Bit Processor

4. Digital Systems: Hardware Organization and Design
11/19/2018
Introduction to DSP’s
Signal Characteristics:
Signals are Physical Quantities:
Signals are Measurable
Signals are Analog
Signals Contain Information.
Examples:
Temperature [oC]
Pressure [Newtons/m2] or [Pa]
Mass [kg]
Speed [m/s]
Acceleration [m/s2]
Torque [Newton*m]
Voltage [Volts]
Current [Amps]
Power [Watts]
In this class, analog signals are electrical.
Sensors: are devices that convert other physical quantities (temperature, pressure, etc.) to electrical signals.
19 November 2018
Veton Këpuska
Architecture of a Respresentative 32 Bit Processor

5. Introduction to Signals and Systems
Signal and Systems
Introduction to Signals and Systems

6. Modeling Engineers model two distinct physical phenomena:
Signals are modeled by mathematical functions.
Physical systems are modeled by mathematical equations.
19 November 2018
Veton Këpuska

7. Introduction to Signals and Systems
Introduction to Signals and Systems as related to Engineering
Modeling of physical signals by mathematical functions
Modeling physical systems by mathematical equations
Solving mathematical equations when excited by the input functions/signals.
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8. What are Signals?
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9. Signals
Signals, x(t), are typically real functions of one independent variable that typically represents time; t.
Time t can assume all real values: -∞ < t < ∞,
Function x(t) is typically a real function.
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10. Example of Signals: Speech
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11. Another view:
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12. Speech Signal
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13. 1.5 second of Speech
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14. Example of Signals EKG:
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15. Example of Signals: EEC
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16. Categories of Signals Signals can be: Continuous, or Discrete:
T – sampling rate
f – sampling frequency – 1/T
 – radial sampling frequency – 2f= 2/T
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17. Corrupting, stochastic noise signal
Signal Processing
Signals are often corrupted by (additive) noise.
s(t) = x(t)+n(t)
Want to ‘filter’ the measured signal s(t) to remove undesired noise effects n(t).
Need to retrieve x(t).
Deterministic signal
Corrupting, stochastic noise signal
Signal Processing
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18. What is a System?
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19. Modeling Examples
Human Speech Production is driven by air (input signal) and produces sound/speech (output signal)
Voltage (signal) of a RLC circuit
Music (signal) produced by a musical instrument
Radio (system) converts radio frequency (input signal) to sound (output signal)
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20. Speech Production Human vocal tract as a system:
Driven by air (as input signal)
Produces Sound/Speech (as output signal)
It is modeled by Vocal tract transfer function:
Wave equations,
Sound propagation in a uniform acoustic tube
Representing the vocal tract with simple acoustic tubes
Representing the vocal tract with multiple uniform tubes
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21. Speech Communication Pathway
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22. Anatomical Structures for Speech Production
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23. Digital Systems: Hardware Organization and Design
11/19/2018
Uniform Tube Model
Volume velocity, denoted as u(x,t), is defined as the rate of flow of air particles perpendicularly through a specified area.
Pressure, denoted as p(x,t), and
19 November 2018
Veton Këpuska
Architecture of a Respresentative 32 Bit Processor

24. RLC Circuit Voltage, v(t) input signal Current, i(t) output signal
Inductance, L (parameter of the system)
Resistance, R (parameter of the system)
Capacitance, C (parameter of the system)
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25. Newton’s Second Law in Physics
The above equation is the model of a physical system that relates an object’s motion: x(t), object’s mass: M with a force f(t) applied to it:
f(t), and x(t) are models of physical signals.
The equation is the model of the physical system.
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26. What is a System?
A system can be a collection of interconnected components:
Physical Devices and/or
Processors
We typically think of a system as having terminals for access to the system:
Inputs and
Outputs
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27. Example: Single Input/Single Output (SISO) System+ +
Electrical Network
Vin
Vout - -
Multiple Input/Multiple Output (MIMO) System
x1 (t)
y1 (t)
x2 (t)
System
y2 (t) … …
xp (t)
yp (t)
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28. Example:
Alternate Block Diagram Representation of a Multiple Input/Multiple Output (MIMO) System
x(t)
y(t)
System
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29. System Modeling Physical System Mathematical Model Model Analysis
Design Procedure
Model Simulation
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30. Model Types Input-Output Description State-Space Description
Frequency-Domain Representations:
Transfer Function - Typically used on ideal Linear-Time-Invariant Systems
Fourier Transform Representation
Time-Domain Representations
Differential/Difference Equations
Convolution Models
State-Space Description
Time-Domain Representation
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31. Model Types Continuous Models Discrete Models 19 November 2018
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32. Digital Systems: Hardware Organization and Design
11/19/2018
Introduction to DSP’s
Analog  Continuous
Not of interest
Discrete Models: DSP process digital signals:
Analog-to-Digital Converter (ADC)
Binary representation of the analog signal
Digital-to-Analog Converter (DAC)
Digital representation of the signal is converted to continuous analog signal.
19 November 2018
Veton Këpuska
Architecture of a Respresentative 32 Bit Processor

33. Analog Low-pass Filter
ADC
a) Continuous Signal
b) Amplitude Quantized Signal fs
x(t)
Analog Low-pass Filter
Sample and
Hold
xa(nT)
Quantizer
DSP
x[n]
c) Amplitude & Time Quantized – Digital Signal
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34. Digital Systems: Hardware Organization and Design
11/19/2018
Example of ADC
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Architecture of a Respresentative 32 Bit Processor

35. Analog Low-pass Filter
DAC
a) Digital Output Signal
b) Analog Signal
c) Continuous Low-pass filtered Signal
DSP
Digital to Analog
Converter
Analog Low-pass Filter
y(t)
y[n]
ya(nT)
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36. Why Processing Signals?
Digital Systems: Hardware Organization and Design
11/19/2018
Why Processing Signals?
Extraction of Information
Amplitude
Phase
Frequency
Spectral Content
Transform the Signal
FDMA (Frequency Division Multiple Access)
TDMA (Time Division Multiple Access)
CDMA (Code Division Multiple Access)
Compress Data
ADPCM (Adaptive Differential Pulse Code Modulation)
CELP (Code Excited Linear Prediction)
MPEG (Moving Picture Experts Group)
HDTV (High Definition TV)
Generate Feedback Control Signal
Robotics (ASIMOV)
Vehicle Manufacturing
Process Control
Extraction of Signal in Noise
Filtering
Autocorrelation
Convolution
Store Signals in Digital Format for Analysis
FFT …
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Architecture of a Respresentative 32 Bit Processor

37. Digital Telephone Communication System Example:
Digital Systems: Hardware Organization and Design
11/19/2018
Digital Telephone Communication System Example:
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Architecture of a Respresentative 32 Bit Processor

38. Typical Architecture of a DSP System
Digital Systems: Hardware Organization and Design
11/19/2018
Typical Architecture of a DSP System
Analog Signal Processing
Analog Signal Conditioning
Sensor
Digital Signal Processing
Digital Signal Conditioning
ADC
DSP
DAC
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Architecture of a Respresentative 32 Bit Processor

39. SASE_Lab Example
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40. SASE_Lab Example (cont.)
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41. SASE_Lab Example (cont.)
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42. Digital Systems: Hardware Organization and Design
11/19/2018
Why Using DSP?
Low-pass Filtering example:
Analog Filter: Chebyshev Type I and Order 6, vs.
Digital Filter: FIR 129-Tap Filter
19 November 2018
Veton Këpuska
Architecture of a Respresentative 32 Bit Processor

43. Analog Filter: Chebyshev of Type I
Digital Systems: Hardware Organization and Design
11/19/2018
Analog Filter: Chebyshev of Type I
Chebyshev Type I (Pass-Band Ripple)
6-Pole
1.0 dB Pass-Band Ripple
Non-liner Phase
MATLAB: fdatool
Order = 6
Fs = 10,000 Hz
Fpass = 1,000 Hz
Apass = 1 [dB]
19 November 2018
Veton Këpuska
Architecture of a Respresentative 32 Bit Processor

44. Example of a 3-rd order Active low-pass filter implementation
Digital Systems: Hardware Organization and Design
11/19/2018
Example of a 3-rd order Active low-pass filter implementation
19 November 2018
Veton Këpuska
Architecture of a Respresentative 32 Bit Processor

45. Magnitude Response of Chebyshev Filter Type I Order 6.
Digital Systems: Hardware Organization and Design
11/19/2018
Magnitude Response of Chebyshev Filter Type I Order 6.
19 November 2018
Veton Këpuska
Architecture of a Respresentative 32 Bit Processor

46. Digital Systems: Hardware Organization and Design
11/19/2018
Pass-Band Ripple 1.0 dB
19 November 2018
Veton Këpuska
Architecture of a Respresentative 32 Bit Processor

47. Digital Systems: Hardware Organization and Design
11/19/2018
Digital Filter Design
FIR,
129-Tap,
Less then dB Pass Band Ripple
Linear Phase
19 November 2018
Veton Këpuska
Architecture of a Respresentative 32 Bit Processor

48. FIR Filter Magnitude Response
Digital Systems: Hardware Organization and Design
11/19/2018
FIR Filter Magnitude Response
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Architecture of a Respresentative 32 Bit Processor

49. Less then 0.002 dB Pass-Band Ripple
Digital Systems: Hardware Organization and Design
11/19/2018
Less then dB Pass-Band Ripple
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Architecture of a Respresentative 32 Bit Processor

50. Analog vs. Digital Implementations
Digital Systems: Hardware Organization and Design
11/19/2018
Analog vs. Digital Implementations
Analog
Cons:
Approximate Filter Coefficients
Only standard components available
Environment Temperature dependent
Less accurate
Can be used only for designed purpose
Pros:
Operate in real-time
Digital (DSP)
Cons:
Real-time operation is dependent on the speed of processor and the complexity of problem at hand.
Pros:
Accurate Filter implementation to desired precision
Operation independent on the environment.
Flexible
DSP’s can be reprogrammed.
19 November 2018
Veton Këpuska
Architecture of a Respresentative 32 Bit Processor

51. DSP Implementation of the FIR Filter
Digital Systems: Hardware Organization and Design
11/19/2018
DSP Implementation of the FIR Filter
129-tap digital filter requires 129 multiply-accumulates (MAC)
Operation must be completed within sampling interval (1/Fs) to maintain real-time.
Fs=10000Hz = 10kHz ⇒ 100 s
ADSP-21xx family performs MAC process in single instruction cycle
Instruction rate > 129/100 s = 1.3 MIPS
ADSP-218x 16-bit fixed point series: 75 MIPS.
19 November 2018
Veton Këpuska
Architecture of a Respresentative 32 Bit Processor

52. End