1. Processor Architectures
Lorenz Sauer
2003/04

2. Synopsis
 History of Computing
 Principal Architecture
 Von Neuman Architecture
 RISC, CISC, VLIW
 SIMD, SISD, MISD, MIMD
• Survey
• Outlook

3. History of Computing
 1st Switches
 2nd Binary Theory (comprehensive)
 Implemented as:
 Tubes
 Transistor
 BiPolar
 FET (especially MOSFET)
 Future (DNA,...)

4. Hardware-means to the end...
 Tube Technology
 huge, high power dissipation, very slow
 Transistor
 First models: huge
 Minaturization: rapidly
 BiPolar: fast, decent power dissipation
 FET: decent speed, low power dissipation,
extreme Intregration Density is possible

5. Computing Timetable
 1949 John von Neumann
 1970s first microprocessor machines
 1980s IBM PC settling in industry
 1990s large-scale mainframes
 2000s GRID, interconnected computing
 >2000

6. History: VisiCalc the Killer App
 1979: First released with Apple II
 1981: Ported to IBM PCs
 Convinces the „industry„ of IBM PCs
 New mainstream market born
 Still executable
 27.520bytes of
Spreadsheetness

7. Principal Architecture: Basics
 Processor or Central Processing Unit
 CPU is the heart of a computer
 Execute programs stored in main memory
 Instructions are processed sequentially:
 fetched, examined and executed
 Church–Turing thesis
 The CPU is composed of:
 Control Unit
 Arithmetic logic unit (ALU)
 Registers

8. Principal Architecture
Central Unit
CPU
(Central Processing Unit)
Calculator Controller
ALU CU
(Arithmetical Logical Unit) (Control Unit)
Bus System
Memory Input/Output
(Addressing Unit) (IO Unit)

9. Instruction Execution
 Pure VN(Von Neuman)-Execution Model
 Nowadays few computers employ pure von
Neumann architecture
 No Check for Interrupts
 Pure VN computers spend a lot of time
moving data from and to memory
 So called “Neumann bottleneck“

10. Instruction Execution
 Advanced VN Model(s)
 Interrupt built in
 Bus Architecture extended over several
busses (different stepping possible)
 Pipelining
 Caching
 Co-Processor (Math, DSP,..)
 Parallelization of Units

11. Example of Advanced VN Model
Central Unit
CPU
(Cent ral P rocessing Unit )
Calculator
Register set Controller Addressing
ALU AU
CU
(Arit hmet ical Logical
(Regist er File) (Cont rol Unit ) (Address Unit )
Unit )
L1-Cache
BIU
(Bus Int erface Unit )
Controlbus Databus Addressbus

12. The Instruction Set
 Instruction set:
 “Collection of all instructions used to communicate
with the CPU“
 sizes vary from 20 to 300+ instructions
 Determined upon the type of machine
 larger instruction sets not necessarily better
 tailored to the use (of the processor)
 Compilers generate many machine
instructions (Ops) from a highlevel language
statement
 Most common are: CISC, RISC, VLIW
 Complex / Reduced instruction set computing, VLIW
~long instructions used in parallelism: see MIMD

13. Processor: Typical Architectures
 SISD
S...Single
 MISD M...Multiple
 SIMD I...Instruction
D...Data
 MIMD

14. SISD
 Single Instruction Single Data
 Almost any conventional PC is SISD
 VN-model is a pure SISD

15. MISD
 Multiple Instruction Single Data
 No commercial success
 Example: Systolic Processor

16. SIMD
 Single Instruction Multiple Data
 Executes operations in parallel
 Example: Vector computer aka Array
computer (~history of supercomputers)
 Nowadays as SIMD extensions
 Speeds up certain applications: chiefly
multimedia (~rich in single precision
floating point data)

17. MIMD
 Multiple Instruction Multiple Data
 Parallel architecture
 Many functional units
 Performs different operations on
seperate data
 Example: Multiprocessor,
interconnected workstations

18. Other: Vector-, Array-Processor
 Common in supercomputers till 1980s
 performs operations in parallel
 Copes well with large data chunks
 Bad under general purpose conditions
 Nowadays in PC-CPUs as SIMD

19. Other: Artificial Neural Processor
 Employed for pattern recognition
 Artificial Neural Network(ANN) model
 mutually linked, homogeneous
processing units
 Units perform basic ANN operations:
 Threshold calculation
 Weighting
 Addition,....

20. Other: Parallel Reduction Machine
 Simplification of expressions
 Expressions reshaped into smaller,
partial ones
 Obtained through recursion of partial
expressions
 Performs reduction programs
 String reduction vs. Graph reduction
machines

21. Other: Systolic Processor
 Array of Processing units (Cells)
 Single cell trivial
 Relay data via n - I/O
 Structure: Rectangular, Hexagonal or
triangular
 Elements process same calculation
 Edge cells are the main I/O

22. Other: Fuzzy Processor
 Based on fuzzy logic
 many-valued logic or probabilistic logic
 approximate values rather than fixed (0|1)
 “set of approximate rules”-logic:
 IF variable IS ~property THEN response 1
 IF variable IS >>property THEN response 2
 Examples:
 Washing maschines,Auto focus,...

23. Other: Digital Signal Processor
 Used for very specific tasks
 Implements algorithms in Hardware
 Very fast at specific tasks
 Useless for general purpose programs
 Sufficient for some applications
 Can lower overall costs

24. Survey: GRID Computing
 Used in scenarios to big for single
supercomputers
 Heterogenous structure
 Heterogenous computer-hardware / software and
structure scattered around the globe
 Common middleware necessary
 E.g. Globus Toolkit
 2 Types, determined by their use:
 Computation Grids
 Data Grids
 Examples:
 SETI Project, @Folding: Protein folding...

25. Outlook
 Processor Optimization
 DNA Computer
 Quantum Computer

26. Outlook: Processor Optimization
 Clock Speeds
 Minaturization
 Improved & extended architecture
 Compilers (good at trvial tasks, fail at
more complicated and parallel tasks)
 Non-trivial to determine the use of
instruction extensions
 Most unit extensions are not used

27. Outlook: DNA Computer
 Concept from 1994
 DNA used as logical gates
 Input: Code as genetic fragments
 Output: spliced fragments
 More or less theoretical (as of yet)
 Estimated to surpass any conventional
PC in some bioinformatic tasks

28. Outlook: Quantum Computer
 1981: Quantum computer theory
 Bits vs QBits
 Difficult to generate and maintain,
due to outside effects
 8 bit Computer is in 1 state of 256
 8 Qbit Computer is in n state(s) of 256
 Superposition of states
 Quantum parallelism
 All values exist; a single value is determined at the time
of measurement
 10Qbit computer could surpass a supercomputer
 Problems of error correction and calculation reliability