Download What is the system?

VLSI Systems--Spring 2009
Introduction:
--syllabus; goals
--schedule
--project
--student survey, group
formation
LEVELS &
VIEWS:
Behavioral / Functional View
Hardware Design
Space
Performance Specs.
HIGH
LEVEL
Algorithms
Register Transfers
Boolean Logic, FSM's
Transfer Functions, Timing
Transistors, Contacts, Wires, Vias
Gates, Flip-flops, Cells
Registers, ALU's, MUX's
Hardware Modules
Processors, Memory,
Switches, Buses
Structural View
Layout Geometry
Modules, Cells
Floor Plans
Clusters
Physical Partitions
Physical View
Issues:
--timing
--synch/asynch
--parallelism
--digital/analog
--space
--power
“Optimal” processor:
speed
size
I/O
power
cost
flexibility
(parameterization,
new technology)
………
Designing the processor:
--basic design
--parameterization
--valid experiments
Processor levels:
--system specification
--architecture (modules)
--logic level
--(physical level)
Design process: Linear Sequential Model (“waterfall model”): Sequential
approach from system level through analysis, design, coding, testing, support-oldest and most widely used paradigm
Analysis
Design
Code
Test
Modern
design is incremental:
top level— requirements, specify, design for test
requirements, specify, design for test
Maintain
implement, TEST
implement, test
………………………………………………
lowest level: req, spec, design for test, IMPLEMENT, TEST
Example: A System
Factors
(Experimental Conditions)
System
Inputs
System
(“Black Box”)
System
Outputs
Responses
(Experimental Results)
Experimental Research
Define
System
Identify
Factors
and Levels
Define system outputs first
● Then define system inputs
● Finally, define behavior (i.e., transfer function)
●
Identify system parameters that vary (many)
● Reduce parameters to important factors (few)
● Identify values (i.e., levels) for each factor
●
Identify
Response(s)
●
Identify time, space, etc. effects of interest
Design
Experiments
●
Identify factor-level experiments
Introduction--The Three Faces of the Experimenter
I have to try every
combination of problem
instance and
treatment. I’ll NEVER
meet the conference
deadline.
I tried my treatment
on one carefully
chosen problem
instance. It
MUST be the best
treatment.
Problem Instance /
Treatment Space
I used well-established
statistical techniques
and design of
experiments
to minimize cost of the
experiments and to
maximize confidence in
the results.
Create and Execute System; Analyze Data
Define
Workload
Create
System
Execute
System
Analyze &
Display
Data
Workload can be a factor (but often isn't)
● Workloads are inputs that are applied to
system
●
Create system so it can be executed
● Real prototype
● Simulation model
● Empirical equations
●
Execute system for each factor-level binding
● Collect and archive response data
●
Analyze data according to experiment design
● Evaluate raw and analyzed data for errors
● Display raw and analyzed data to draw
conclusions
●
Some Examples
Analog Simulation
– Which of three solvers is
best?
– What is the system?
– Responses
• Fastest simulation
time
• Most accurate result
• Most robust to types
of circuits being
simulated
– Factors
• Solver
• Type of circuit model
• Matrix data structure
Epitaxial growth
– New method using nonlinear temp profile
– What is the system?
– Responses
• Total time
• Quality of layer
• Total energy required
• Maximum layer
thickness
– Factors
• Temperature profile
• Oxygen density
• Initial temperature
• Ambient temperature
SUMMARY—15 IMPORTANT POINTS FOR EXPERIMENTERS:
1. Even careful experimentation and observation may miss important facts; new experiments may cause old
conclusions to be discarded; EXPERIMENTS ARE NOT PROOFS.
2. It is just as important to report NEGATIVE results as to report POSITIVE results. The experimenter must
always accurately record and thoroughly report ALL results.
3. IGNORING IMPORTANT FACTORS CAN LEAD TO ERRONEOUS CONCLUSIONS, SOMETIMES WITH
TRAGIC RESULTS.
4. YOUR RESULTS ARE ONLY VALID FOR THE PART OF THE DATA-TREATMENT SPACE YOU HAVE
EXPLORED; YOU CANNOT CLAIM KNOWLEDGE OF WHAT YOU HAVE NOT EXPLORED
5. An experiment is worthless unless it can be REPEATED by other researchers using the same experimental
setup; experimenters have a duty to the research community to report enough about their experiment and data so
that other researchers can verify their claims
6. YOU ONLY GET ANSWERS TO THE QUESTIONS YOU ASK
7. if your are going to use a (pseudo-)RANDOM NUMBER GENERATOR, make sure the output behaves enough like a sequence of
TRUE RANDOM NUMBERS
8. An experiment must be repeated a SUFFICIENT NUMBER OF TIMES for the results to be attributed to more
than random error
9. Choosing the CORRECT MEASURE for the question you are asking is an important part of the experimental
design
10. Reporting CORRECT results, PROPERLY DISPLAYED, is an integral part of a well-done experiment
11. MISUSE OF GRAPH LABELING can lead to MISLEADING RESULTS AND INCORRECT CONCLUSIONS
12. INTERPOLATING your results to regions you have not explored can lead to INCORRECT CONCLUSIONS
13. IGNORING the “NULL HYPOTHESIS” when reporting your results can be very misleading
14. Don’t mistake CORRELATION for DEPENDENCE
15. Justify your choice of CURVE using VALID STATISTICS, not “appearance”
Also important:
Future technology
example: itrs roadmap