Moore's Law and the Future of Technology: Exponential Growth Trends and Challenges, Slides of Introduction to Computers

Download Moore's Law and the Future of Technology: Exponential Growth Trends and Challenges and more Slides Introduction to Computers in PDF only on Docsity!

Riding the Technology Curve

Dec. 1, 1998

Topics Topics

n Moore’s Law

n Are exponential problems

intractable?

n Impact on real-world problems

n The verification challenge

Impact of Technology Impact of Technology

It’s the Technology, Stupid! It’s the Technology, Stupid!

n Computer science has ridden the wave

Things Aren’t Over Yet Things Aren’t Over Yet

n Technology will continue to progress along current growth

curves

n For at least 10 more years

n Difficult technical challenges in doing so

Even Technologists Can’t Beat Laws of Physics Even Technologists Can’t Beat Laws of Physics

Semiconductor Industry Forecast Semiconductor Industry Forecast

n Semiconductor Industry Association, 1992 Technology

Workshop

Year 1992 1995 1998 2001 2004 2007

Feature size 0.5 0.35 0.25 0.18 0.12 0.

DRAM cap 16M 64M 256M 1G 4G 16G

Gates/chip 300K 800K 2M 5M 10M 20M

Chip cm^2 2.5 4.0 6.0 8.0 10.0 12.

I/Os 500 750 1500 2000 3500 5000

off chip MHz 60 100 175 250 350 500

on chip MHz 120 200 350 500 700 1000

• 5 –

Impact of Moore’s Law Impact of Moore’s Law

Moore’s Law Moore’s Law

n Performance factors of systems built with integrated circuit

technology follow exponential curve

n E.g., computer speed / memory capacities double every 1.

years

Implications Implications

n Computers 10 years from now will run 100 X faster

n Problems that appear intractable today will be

straightforward

n Must not limit future planning with today’s technology

Example Application Domains Example Application Domains

n Speech recognition

l Will be routinely done with handheld devices

n Breaking secret codes

l Need to use large enough encryption keys

Solving with a Y2K Computer Solving with a Y2K Computer

Y2K Computer

1.E-

1.E-

1.E-

1.E+

1.E+

1.E+

1.E+

1.E+

1.E+

1.E+

1.E+

1.E+

1.E+

1.E+

1.E+

1.E+

1.E+

1.E+

1.E+

10 20 30 40 50 60 70 80 90 100 Problem Size (n)

CPU Years

(^) second minute hour day week year

Time per Operation

Moore’s Law Computer Moore’s Law Computer

Operation Operation

n Start computing on Jan. 1, 2000

n Keep upgrading machine being used

n In year y , would have performance 1.587 y^ relative to Y2K

machine

Performance Performance

n After y years of operation, would have performed as much

computation as Y2K machine would do in time:

n Examples

y = 1 1. y = 2 3. y = 5 20. y = 10 218. y = 100 2.53 X 10^20

01.^587

y

y (^) x dx

Solving with a Moore’s Law Computer Solving with a Moore’s Law Computer

Moore'sLawComputer

0

20

40

60

80

100

120

140

160

10 20 30 40 50 60 70 80 90 100 Problem size (n)

CPU Years

(^) second minute hour day week year

Tmie per Operation

• 11 –

Effect of Step Complexity Effect of Step Complexity

Observe Observe

n Step complexity k adds only additive factor of 2.16 ln k to

running time

Example Example

n For n = 100

k y

1 second 111

1 minute 120

1 hour 129

1 day 136

1 week 140

1 year 148

Explanation Explanation

n Final years of computation will be on exponentially faster

machines

How to Be a Visionary How to Be a Visionary

Pick a Really Hard Problem Pick a Really Hard Problem

n Sequencing of human genome

n Accurate weather prediction

n Flying helicopter autonomously

Make Proclamations Make Proclamations

n “In 20 years, problem X will be solved”

Wait Wait

n But make sure everyone credits you with the vision

n Maybe make a few contributions to technology

Amass Glory Amass Glory

n Turing Award Citation:

l “He/She had the foresight to see that this problem could be solved.”

Truly Hard Problems Truly Hard Problems

Those That Get Harder over Time Those That Get Harder over Time

n Track Moore’s law growth

n How do I make sure my chip will operate correctly?

n How do I make sure my programs are correct?

n How do I manufacture state-of-the-art chips?

Highlight Highlight

n Research at CMU on formal verification of hardware

The Pentium Fiasco The Pentium Fiasco

Events Events

n Prof. Thomas Nicely, Lynchburg College, VA

l Looking at properties of “twin primes” l Incorrect reciprocals for 824633702441 and 824633702443 » ~ Single precision accuracy (4 X 10–9) l Contacted others on Oct. 30, ‘

n Spreading of Information on Internet news group

comp.sys.intel

l Terje Mathisen of Norway posts Nicely’s findings on Nov. 3 l Andreas Kaiser of Germany finds 23 bad reciprocals, Nov. 10

n Tim Coe, Vitesse Semiconductor, Nov. 16

l Created (good enough) software model of flawed divide algorithm l Discovered (nonreciprocal) cases with errror up to 6 X 10– l Later showed 1738 mantissa pairs with less than single precision accuracy » out of 7.4 X 10^13 single precision mantissa pairs

Resolution Resolution

Free Replacement Policy, Dec. 20 Free Replacement Policy, Dec. 20

n No need to argue need

n Complex logistics

l Many different versions l Actual replacement easy

Financial Impact Financial Impact

n Intel charged $475 million to it’s 4Q94 earnings

n Still was 2nd most profitable year ever

n Few companies could survive such an expensive mistake

n In the end, generated lots of valuable PR for Intel

CMU’s Research Contributions CMU’s Research Contributions

Symbolic Model Checking Symbolic Model Checking

n Developed by Ken McMillan while CMU PhD student

l Building on work by advisor Ed Clarke

n Verify properties of finite state systems with 10^20 or more

states

Binary Moment Diagrams Binary Moment Diagrams

n Developed by Bryant & Chen in 1994.

n Symbolic representation of functions having bit-level inputs

and numeric outputs

n Compact for common logical and arithmetic operations

Word-Level Model Checking Word-Level Model Checking

n Developed by Xudong Zhao while CMU PhD student

l Advisor Ed Clarke

n Allow specification to contain arithmetic relations among

words of data

Temporal Logic Model Checking Temporal Logic Model Checking

Verifying Reactive Systems Verifying Reactive Systems

n Construct state machine representation of reactive system

l Nondeterminism expresses range of possible behaviors l “Product” of component state machines

n Express desired behavior as formula in temporal logic

n Determine whether or not property holds

Traffic Light Controller Design

Traffic Light Controller Design

“It is never possible to have a green light for both N-S and E-W.”

Model Checker

True

_False

• Counterexample_