INTRODUCTION TO
Microprocessors
Dr. Hugh Blanton
ENTC 4337/5337
Objectives
• Build intuition for signal processing
concepts
• Translate signal processing concepts into
real-time digital communications software
in laboratory
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General Information
• Contact Information
• [email protected]
• Phone—(423) 439-4177
• Web Page
• http://faculty.etsu.edu/blanton
• Office Hours
• MWF—10:15-11:45
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Grading
• Calculation of numeric grades
•
•
•
•
15% midterm #1
25% final #2 (semi-cumulative)
20% homework
50% laboratory
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• Laboratory component
• Students work in teams of two on lab
assignments/reports
• Assign team members same lab report
grade and then apply individual
deductions for attendance/participation
• Lowest lab report dropped
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Academic Integrity
• Homework/Exam assignments
• Discuss homework/exam questions with
others
• Be sure to submit your own independent
solution
• Turning in two identical (or nearly identical)
homework sets is considered academic
dishonesty
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• Laboratory reports
• Should only contain work of those
named on report
• If any other work is included, then
reference source
• Copying information from another
source without giving proper reference
and quotation is plagiarism
• Source code must be original work
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Topics
• The TMS320C6X (1 week)
• Programming (4 weeks)
•C
• Code Composer
• Assembly Language
• Hardware (4 weeks)
• architecture
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• Applications
• FIR Filters
• IIR Filters
• Fast Fourier Transforms
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TMS320C6x Manuals
• You need to refer to various TMS320C6000
manuals, which are only available
electronically:
• Code Composer User’s Guide
• http://focus.ti.com/lit/ug/spru301c/spru301c.pdf
• Optimizing C Compiler
• http://www-s.ti.com/sc/psheets/spru187k/spru187k.pdf
• Programmer’s Guide
• http://www-s.ti.com/sc/psheets/spru198g/spru198g.pdf
• CPU and Instruction Set Reference Guide
• http://www-s.ti.com/sc/psheets/spru189f/spru189f.pdf
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Microprocessors
• General-purpose processors or
microcontrollers (GPPs/MCUs for
short) are either
• not specialized for a specific kind of
applications (in the case of generalpurpose processors), or
• they are designed for control-oriented
applications (in the case of
microcontrollers).
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Digital Signal Processors
• DSP processors have features designed to
support high-performance, repetitive,
numerically intensive tasks.
• Mostly designed with the same few basic
operations in mind
• They share the same set of basic characteristics
• These characteristics fall into three categories:
• specialized high speed arithmetic
• data transfer to and from the real world
• multiple access memory architecture
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DSP Features
• Features that accelerate performance in DSP
applications include:
• Single-cycle multiply-accumulate (MAC) capability;
• high-performance DSPs often have two multipliers that
enable two multiply-accumulate operations per instruction
cycle;
• some DSP have four or more multipliers
• Specialized addressing modes, for example,
• pre- and post-modification of address pointers,
• circular addressing, and
• bit-reversed addressing
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DSP Features
• Most DSPs provide various configurations of
on-chip memory and peripherals tailored for
DSP applications.
• DSPs generally feature multiple-access memory
architectures that enable DSPs to complete several
accesses to memory in a single instruction cycle
• Specialized execution control.
• Usually, DSP processors provide a loop instruction
that allows tight loops to be repeated without
spending any instruction cycles
• for updating and testing the loop counter or
• for jumping back to the top of the loop
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DSP Features
• DSP processors are known for their irregular instruction
sets, which generally allow several operations to be
encoded in a single instruction.
• For example, a processor that uses 32-bit instructions may
encode
• two additions,
• two multiplications, and
• four 16-bit data moves into a single instruction.
• In general, DSP processor instruction sets allow a data
move to be performed in parallel with an arithmetic
operation.
• GPPs/MCUs, in contrast, usually specify a single operation
per instruction
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DSP Features
• It is worth noting that the difference
between DSPs and GPPs/MCUs is
fading:
• many GPPs/MCUs now include DSP
features, and DSPs are increasingly adding
microcontroller features.
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What is a DSP?
• Digital Signal Processors (DSP) process digital
signals
• An alternative method to process analog world
signals
ADC
DSP
DAC
• Once the signal is in digital form, the DSP can
easily process it
• After the DSP has processed the signal, the output
signal must be converted back to analog so that we
can sense it.
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Why DSP?
• Advantages of digital signal processing
• Programmability—one hardware does many tasks
• Flexibility and upgradeability—develop a new code
• Repeatability—A CD player always plays the same music
quality
• Advantages of analog signal processing
• low cost in some applications—attenuators, amplifiers
• wide bandwidth (GHz)
• Infinite resolution (no quantization error) and low signal levels
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The DSP System
• DSP chip
• Arithmetic Logic Unit (ALU)
• TMS320C6X
Memory
• Memory
• Converters
• Analog-to-Digital
ADC
DSP
DAC
• Digital-to-Analog
• Communication Ports
• Serial
Ports
• Parallel
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Review: Signals
• Continuous-time (analog) signals are
functions of a real argument
• x(t) where t can take any real value
• Discrete-time (digital) signals are functions of
an argument that takes values from a discrete
set x[n]
• n {...-3,-2,-1,0,1,2,3...}
• Integer index n instead of time t for discrete-time
systems
• Value for x may be real or complex
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Analog and Digital Signals
• Amplitude of an analog signal can take
any real or complex value at each time
(sample)
• Amplitude of a digital signal takes
values from a discrete set
1
1
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Analog and Digital Signals
• A system is a transformation from one signal
(called the input) to another signal (called the
output or the response).
• Continuous-time systems with input signal x and
output signal y (a.k.a., the response):
• y(t )  x(t )  x(t  1)
• y(t )  x 2 (t )
• Discrete-time system examples
• y[n]  x[n]  x[n  1]
2
• y[n]  x [n]
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Audio Compact Discs
• Human hearing is from about 20 Hz to 20 kHz
• Sampling theorem: sample analog signal at a rate of
more than twice the highest analog frequency
• Apply a lowpass filter to pass frequencies up to 20 kHz;
• e.g. a coffee filter water (small particles) through a coffee
filter but not coffee grounds (large particles)
• Lowpass filter needs 10% of maximum passband frequency
to roll off to zero (2 kHz rolloff in this case).
• Sampling at 44.1 kHz captures analog frequencies that are
less than 22.05 kHz
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Signal Processing Systems
• Speech synthesis and speech recognition
• Audio CD players
• Audio compression (MP3, AC3)
• Image compression (JPEG, JPEG 2000)
• Optical character recognition
• Video CDs (MPEG 1)
• DVD, digital cable, and HDTV (MPEG 2)
• Wireless video (MPEG 4/H.263)
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Communication Systems
• Voiceband Dialup/Fax modems
• Digital subscriber line (DSL) modems
• ISDN: 144 kilobits per second (kbps)
• Business/symmetric: HDSL and HDSL2
• Home/symmetric: ADSL and VDSL
• Cable modems
• Cell phones
• First generation (1G): AMPS
• Second generation (2G): GSM, IS-95 (CDMA)
• Third generation (3G): cdma2000, WCDMA
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DSP Architectures
• Multiply-Add-Accumulate (MAC) instruction
• Most common operation in DSP,
• A=B*C+D
• Typically 70 clock cycles with ordinary processors
• Single instruction cycle
• Havard architecture
• Separate data memory/bus and program memory/bus
• Multiple memory accesses per instruction cycle
• Modified von Neuman architecture
• multiple memory accesses per instruction cycle by the
simple trick of running the memory clock faster than the
instruction cycle.
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DSP Architectures
• Deterministic interrupt service routine
latency
• Special addressing modes supported in
hardware
• Modulo addressing for circular buffers (e.g. FIR
filters)
• Bit-reversed addressing (e.g. fast Fourier
transforms)
• DSP needs a program that is a series of
instructions that perform certain functions.
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Digital
vs
Analog
Digital Signal Processing
Advantages
Limitations
• Often easier system upgrade.
• A/D & signal processors speed:
wide-band signals still difficult to
treat (real-time systems).
• Data easily stored.
• Finite word-length effect.
• Better control over accuracy
requirements.
• Obsolescence (analog
electronics has it, too!).
• More flexible.
• Reproducibility.
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Impact of DSP on Modern Living
Cellular/mobile telephony
Speech and channel coding
Voice and data processing
Power management
Multipath equaliztion
Automotive
Digital Audio
Digital Radio
Personal communication
systems
Active suspension
Digital audio
Stereo and surround sound
Audio equalization and
mixing
Electronic music
Medical electronics
Critical/intensive care
monitors
Digital X-rays
ECG analyzers
Cardiac monitors
Medical imaging
Personal computer
Sound cards
Data storage and retrieval
Error correction/concealment
Multimedia
Modems
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Analog & digital signals
Analog
Digital
Discrete function Vk of
discrete sampling variable tk,
with k = integer: Vk = V(tk).
0.3
0.3
0.2
0.2
Voltage [V]
Voltage [V]
Continuous function V of
continuous variable t (time,
space etc) : V(t).
0.1
0
-0.1
-0.2
0.1
0
ts ts
-0.1
-0.2
0
2
4
6
time [ms]
8
10
0
2
4
6
8
sampling time, tk [ms]
Uniform (periodic) sampling.
Sampling frequency fS = 1/ tS
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DSP: aim & tools
Applications
• Predicting a system’s output.
• Implementing a certain processing task.
• Studying a certain signal.
• General purpose processors (GPP), -controllers.
Hardware
Software
• Digital Signal Processors (DSP).
Fast
• Programmable logic ( PLD, FPGA ).
Faster
real-time
DSPing
• Programming languages: Pascal, C / C++ ...
• “High level” languages: Matlab, Mathcad, Mathematica…
• Dedicated tools (ex: filter design s/w packages).
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DISP-2003: Introduction to Digital Signal Processing