Next Century Challenges for Computer Science and Electrical Engineering

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Next Century Challenges for Computer Science and
Electrical Engineering

• Professor Randy H. KatzUnited Microelectronics
  Corporation Distinguished Professor
• CS Division, EECS Department
• University of California, Berkeley
• Berkeley, CA 94720-1776 USA

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Agenda

• The Information Age
• EECS Department at Berkeley
• Student Enrollment Pressures
• Random Thoughts and Recommendations
• Summary and Conclusions

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Agenda

• The Information Age
• EECS Department at Berkeley
• Student Enrollment Pressures
• Random Thoughts and Recommendations
• Summary and Conclusions

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A Personal Historical View

• 20th Century as Century of the Electron
• 1884 Philadelphia Exposition--Rise of EE as a
  profession
• 1880s Electricity harnessed for communications,
  power, light, transportation
• 1890s Large-Scale Power Plants (Niagara Falls)
• 1895 Marconi discovers radio transmission/wireles
  s telegraphy
• 1905-1945 Long wave/short wave radio, television
• 1900s-1950s Large-scale Systems Engineering
  (Power, Telecomms)
• 1940s-1950s Invention of the Transistor
  Digital Computer
• 1960s Space program drives electrical component
  minaturization
• 1970s Invention of the Microprocessor/rise of
  microelectronics
• 1980s-1990s PCs and data communications
  explosion
• Power Engineering --gt Communications --gt Systems
  Engineering --gt Microelectronics --gt ???

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Late 20th Century Rise of the Information Age

• Electronics computing information
  technology
• Technologies crucial for manipulating large
  amounts of information in electronic formats
• Hardware Semiconductors, optoelectronics, high
  performance computing and networking, satellites
  and terrestrial wireless communications devices
• Software Computer programs, software
  engineering, software agents
• Hardware-Software Combination Speech and vision
  recognition, compression technologies
• Information industries assemble, distribute, and
  process information in a wide range of media,
  e.g., telephone, cable, print, and electronic
  media companies
• 3 trillion world wide industry by 2010

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View from California on the Importance of
Information Technology

• 35 billion in 1995 sales (vs. 90 billion
  nationwide)
• 27 of computer manufacturing industry
  employment 50 of computer peripheral industry
  employment 37 of nations venture capital
• computers/electronics sector employment 176,400
  software sector employment 104,000
  telecomms/info tech employed 329,000
• Approximately 28 billion for information
  technology RD
• Exports 58.9 billion, more than half of
  Californias total
• Bay region
• 93,000 employed in computers/electronics, 80,000
  in telecomms, 59,000 in multimedia, 30,000
  software jobs in Santa Clara county alone (45,000
  new jobs statewide between 90-95)!
• San Jose dominates NY as highest average wage
  city in country
• Intense political pressure to increase the
  production of students with information
  technology skills

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Software Jobs Go Begging

• Americas New Deficit The Shortage of
  Information Technology Workers, Department of
  Commerce
• Job growth exceeds the available talent
• 1994-2005 1 million new information technology
  workers will be needed
• Help Wanted The IT Workforce Gap at the Dawn of
  a New Century, ITAA
• 190,000 unfilled positions for IT workers
  nationwide
• Between 1986 and 1994, bachelor degrees in CS
  fell from 42,195 to 24,200 (43)

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Robert Luckys Inverted Pyramid
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Agenda

• The Information Age
• EECS Department at Berkeley
• Student Enrollment Pressures
• Random Thoughts and Recommendations
• Summary and Conclusions

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Student and Faculty Statistics

• Faculty
• EE 40.75 FTE
• CS 37 FTE
• Architecture, CAD, Signal Processing, Circuits
  faculty overlap
• 83.75 authorized FTE
• Undergraduate Program
• 893.5 (515 in CS, 378.5 in EE) in B.S. program
• 212 in B.A. program
• 1105.5 total (66 CS, 34 EE)
• Graduate Program
• 300 EE
• 200 CS

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Departmental Culture

• A shared view of computing joining mathematics
  and physics as core of the sciences and
  engineering
• Large-scale interdisciplinary experimental
  research projects with strong industrial
  collaborations
• Architecture RISC, RAID, NOW, IRAM, CNS-1, BRASS
• Parallel Systems Multipole, ScaLAPACK, Spilt-C,
  Titanium
• Berkeley Digital Library Project Environmental
  Data
• InfoPad Portable Multimedia Terminal for
  Classroom Use
• PATH Intelligent Highway Project, FAA Center of
  Excellence
• Computation and algorithmic methods in EE
• Circuit Simulation, Process Simulation, Optical
  Lithography
• CAD Synthesis/Optimization, Control Systems
• Increasing collaboration with other departments
  in Engineering and elsewhere on campus

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Historical Perspective

• Early-mid 1950s Computer engineering activity
  grows within EE department
• Early 1960s Separate CS Department formed within
  College of Letters and Science
• Early 1970s Forced merger--semi-autonomous CS
  Division within single EECS Department separate
  LS CS program for undergraduates continues
• 1980s Strong collaborations between EE and CS in
  VLSI, CAD
• 1990s Increasing interactions between EE
  systems/CS AI/vision EE comms/CS
  networking/distributed systems Intelligent
  Systems/Hybrid Control Systems
• 1994-Present Very rapid growth in CS enrollments
• 1996-1999 First CS Department Chair Goal to
  make symmetric the relationship between EE and CS

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Departmental Structure
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Faculty FTE Breakdown

• EE
• Signal Processing 4.5
• Communication 3.0
• Networks 2.5
• CAD 3.5
• ICs 5.0
• Solid State MEMs 4.5
• Process Tech. Man. 5.0
• Optoelectronics 5.0
• EM Plasma 2.25
• Controls 3.0
• Robotics 2.0
• Bioelectronics (1.3)
• Power 1.5
• TOT 40.75 (1.3 P-in-R)

• CS
• Sci Comp 2.5
• Architecture 5.0
• Software 5.5
• Theory 6.0
• OS/Nets 4.5
• MM/UI/Graphics 4.0
• AI 5.5
• DB 2.0
• TOT 35 2 SOE Lecturers
• DEPARTMENT 77.75 FTE
• 83.75 Authorized (2000)
• 3 New 2 Continue

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Agenda

• The Information Age
• EECS Department at Berkeley
• Student Enrollment Pressures
• Random Thoughts and Recommendations
• Summary and Conclusions

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UG Degree History at Berkeley
Degrees
About half are CS degrees
Year
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Undergraduate Enrollment Trends
Total
EECS/EE
CS Total
EECS/CS
LS CS
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College of Engineering Growth

• Demand for CS skills far exceeds supply in
  California
• University administration and Governor Wilson
  targets student and faculty growth in CS and
  engineering
• Thrust at Berkeley is Bioengineering, Computer
  Science, and Engineering Science (Computational
  Engineering) across the College
• EECS to accept 140 additional students in return
  for 6-8 new FTE over four years

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A New Vision for EECS

• If we want everything to stay as it is, it will
  be necessary for everything to change.
• Giuseppe Tomasi Di Lampedusa (1896-1957)

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Old View of EECS
EE physics circuits signals control
CS algorithms programming comp systems AI
Physical World
Synthetic World
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New View of EECS
EECS complex/electronics systems
Intelligent Sys Control Communications Sys
Intelligent Displays
Reconfigurable Systems Computing
Systems Multimedia User Interfaces
EE components
CS algorithms
Signal Proc Control
AI Software
Robotics/Vision InfoPad IRAM
Programming Databases CS Theory
Processing Devices MEMS Optoelectronics Circuits
CAD Sim Viz
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Design Sci
MechE Sensors Control
Info Mgmt Systems
EECS
Physical Sciences/ Electronics
Cognitive Science
Materials Science/ Electronic Materials
Computational Sci Eng
BioSci/Eng Biosensors BioInfo
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Observations

• Introduction to Electrical Engineering course is
  really introduction to devices and circuits
• Freshman engineering students extensive
  experience with computing significantly less
  experience with physical systems (e.g., ham
  radio)
• Insufficient motivation/examples in the early EE
  courses excessively mathematical and
  quantitative
• These factors drive students into the CS track

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Curriculum Redesign

• EECS 20 Signals and Systems
• Every EECS student will take
• Introduction to Signals and Systems
• Introduction to Electronics
• Introduction to Computing (3 course sequence)
• Computing emerges as a tool as important as
  mathematics and physics in the engineering
  curriculum
• More freedom in selecting science and mathematics
  courses
• Biology becoming increasing important

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EECS 20 Structure and Interpretation of Systems
and Signals

• Course Format Three hours of lecture and three
  hours of laboratory per week.
• Prerequisites Basic Calculus.
• Introduction to mathematical modeling techniques
  used in the design of electronic systems.
  Applications to communication systems, audio,
  video, and image processing systems,
  communication networks, and robotics and control
  systems. Modeling techniques that are introduced
  include linear-time-invariant systems, elementary
  nonlinear systems, discrete-event systems,
  infinite state space models, and finite automata.
  Analysis techniques introduced include frequency
  domain, transfer functions, and automata theory.
  A Matlab-based laboratory is part of the course.

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Topics Covered

• Sets
• Signals
• Image, Video, DTMF, Modems, Telephony
• Predicates
• Events, Networks, Modeling
• Frequency
• Audio, Music
• Linear Time Invarient Systems
• Filtering
• Sounds, Images
• Convolution

• Transforms
• Sampling
• State
• Composition
• Determinism
• State Update
• Examples
• Modems, Speech models, Audio special effects,
  Music

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EE 40 Introduction to Microelectronics Circuits

• Course Format Three hours of lecture, three
  hours of laboratory, and one hour of discussion
  per week.
• Prerequisites Calculus and Physics.
• Fundamental circuit concepts and analysis
  techniques in the context of digital electronic
  circuits. Transient analysis of CMOS logic gates
  basic integrated-circuit technology and layout.

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CS 61A The Structure and Interpretation of
Computer Programs

• Course Format 3 hrs lecture, 3 hrs discussion,
  2.5 hrs self-paced programming laboratory per
  week.
• Prerequisites Basic calculus some programming.
  
• Introduction to programming and computer science.
  Exposes students to techniques of abstraction at
  several levels (a) within a programming
  language, using higher-order functions, manifest
  types, data-directed programming, and
  message-passing (b) between programming
  languages, using functional and rule-based
  languages as examples. It also relates these to
  practical problems of implementation of languages
  and algorithms on a von Neumann machine. Several
  significant programming projects, programmed in a
  dialect of LISP.

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CS 61B Data Structures

• Course Format 3 hrs lecture, 1 hr discussion, 2
  hrs of programming lab, average of 6 hrs of
  self-scheduled programming lab per week.
• Prerequisites Good performance in 61A or
  equivalent class.
• Fundamental dynamic data structures, including
  linear lists, queues, trees, and other linked
  structures arrays strings, and hash tables.
  Storage management. Elementary principles of
  software engineering. Abstract data types.
  Algorithms for sorting and searching.
  Introduction to the Java programming language.

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CS 61C Machine Structures

• Course Format 2 hrs lecture, 1 hr discussion,
  average of six hrs of self-scheduled programming
  laboratory per week.
• Prerequisites 61B.
• The internal organization and operation of
  digital computers. Machine architecture, support
  for high-level languages (logic, arithmetic,
  instruction sequencing) and operating systems
  (I/O, interrupts, memory management, process
  switching). Elements of computer logic design.
  Tradeoffs involved in fundamental architectural
  design decisions.

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Five Undergraduate Programs

• Program I Electronics
• Electronics
• Integrated Circuits
• Physical Electronics
• Micromechanical Systems
• Program II Communications, Networks, Systems
• Computation
• Bioelectronics
• Circuits and Systems
• Program III Computer Systems
• Program IV Computer Science
• Program V General

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Agenda

• The Information Age
• EECS Department at Berkeley
• Student Enrollment Pressures
• Random Thoughts and Recommendations
• Summary and Conclusions

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Departments Strategic Plan

• Human Centered Systems
• User Interfaces Image, graphics, audio, video,
  speech, natural language
• Information Management Intelligent Processing
• Embedded and Network-connected computing
• Hardware building blocks DSP, PGA, Comms
• High performance, low power devices, sensors,
  actuators
• OS and CAD
• Ambient/Personalized/Pervasive Computing

• Software Engineering
• Design, development, evolution, and maintenance
  of high-quality complex software systems
• Specification verification
• Real time software
• Scalable algorithms
• Evolution maintenance of legacy code

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President Clintons IT2 Initiative

• Software
• Software Engineering
• End-User Programming
• Component-Based Software Development
• Active Software
• Autonomous Software
• HCI and Info Mgmt
• Speech/Natural Language
• Information Visualization
• Scalable Info Infrastructure
• Deeply Networked Sys
• Anytime, Anywhere Connect
• Net Modeling/Simulation

• High End Computing
• Improving perform/efficiency of supercomputers
• Creating a computation grid
• Revolutionary computing
• Advanced Computing for Science/Engineering
• Advanced Infrastructure
• Advanced Science Engineering Computation
• Computer Science Enabling Technology
• National Information Infrastructure Applications
• Economic/Social Impacts of IT

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21st Century Challenge for Computer Science

• Avoid the mistakes of academic Math departments
• Mathematics pursued as a pure and esoteric
  discipline for its own sake (perhaps unlikely
  given industrial relevancy)
• Faculty size dictated by large freshman/sophomore
  program (i.e., Calculus teaching) with relatively
  few students at the junior/senior level
• Other disciplines train and hire their own
  applied mathematicians
• Little coordination of curriculum or faculty
  hiring
• Computer Science MUST engage with other
  departments using computing as a tool for their
  discipline
• Coordinated curriculum and faculty hiring via
  cross-departmental coordinating councils

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21st Century Challenges for Electrical Engineering

• Avoid the trap of Power Systems Engineering
• Student interest for EE physical areas likely to
  continue their decline (at least in the USA),
  just when the challenges for new technologies
  becoming most critical
• Beginning to see the limits of semiconductor
  technology?
• What follows Silicon CMOS? Quantum dots?
  Cryogenics? Optical computation? Biological
  substrates? Synthesis of electrical and
  mechanical devices beyond transistors
  (MEMS/nanotechnology)
• Basic technology development, circuit design and
  production methods
• Renewed emphasis on algorithmic and mathematical
  EE Signal Processing, Control, Communications
• More computing systems becoming
  application-specific
• E.g., entertainment, civilian infrastructure (air
  traffic control),

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21st Century Challenges for EE and CS

• 21st Century to be Century of Biotechnology?
• Biomimetics What can we learn about building
  complex systems by mimicing/learning from
  biological systems?
• Hybrids are crucial in biological systems Never
  depend on a single group of software developers!
• Reliability is a new metric of system performance
• Human Genome Project
• Giant data mining application
• Genome as machine language to be reverse
  engineered
• Biological applications of MEMS technology assay
  lab-on-a-chip, molecular level drug delivery
• Biosensors silicon nose, silicon ear, etc.
• What will be more important for 21st century
  engineers to know more physics or more biology?

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Example Affymetrixwww.affymetrix.com

• Develops chips used in the acquisition, analysis,
  management of genetic information for
  biomedical research, genomics, clinical
  diagnostics
• GeneChip system disposable DNA probe arrays
  containing specific gene sequences, instruments
  to process the arrays, bioinformatics software
• IC company? Software company? Bioengineering
  company? Biotech company?

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Should EE and CS Be Separate Departments?

• EEs need extensive computing will spawn
  competing Computer Engineering activity anyway
• Much productive collaborative at intersection of
  EE and CS CAD, Architecture, Signal Processing,
  Control/Intelligent Systems, Comms/Networking
• But all quantitative fields are becoming as
  computational as EE e.g., transportation systems
  in CivilEng
• Will natural center of gravity of CS move towards
  cognitive science, linguistics, economics,
  biology?

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Agenda

• The Information Age
• EECS Department at Berkeley
• Student Enrollment Pressures
• Random Thoughts and Recommendations
• Summary and Conclusions

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Summary and Conclusions

• Fantastic time for the IT fields of EE and CS
• As we approach 2001, we are in the Information
  Age, not the Space Age!
• BUT, strong shift in student interest from the
  physical side of EE towards the algorithmic side
  of CS
• Challenge for CS
• Avoid mistakes of math as an academic discipline
• Coordinate with other fields as they add
  computing expertise to their faculties
• Challenge for EE
• What will be the key information system
  implementation technology of 21st century?
• Challenge for EE and CS
• How to contribute to the Biotech revolution of
  the next century