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Next Century Challenges for Computer Science and Electrical Engineering. Professor Randy H. Katz United Microelectronics Corporation Distinguished Professor CS Division, EECS Department University of California, Berkeley Berkeley, CA 94720-1776 USA. Agenda. The Information Age

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Next century challenges for computer science and electrical engineering l.jpg

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 Engineering

  • The Information Age

  • EECS Department at Berkeley

  • Student Enrollment Pressures

  • Random Thoughts and Recommendations

  • Summary and Conclusions


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Agenda Engineering

  • 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 Engineering

  • 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/wireless 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 --> Communications --> Systems Engineering --> Microelectronics --> ???


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Late 20th Century Rise of the Engineering“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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Software Jobs Go Begging Engineering

  • “America’s 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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Information Engineering

Technology

Software

Applications Software

Middleware Software

Embedded Software

Algorithms

System Software

Hardware

FPGA Design

VLSI Design

Circuit Design

Device Design

Process

Design

Technology

Physics

Increasing Numbers

of Practitioners

Robert Lucky’s Inverted Pyramid


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Agenda Engineering

  • The Information Age

  • EECS Department at Berkeley

  • Student Enrollment Pressures

  • Random Thoughts and Recommendations

  • Summary and Conclusions


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Departmental Culture Engineering

  • 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 Engineering

  • 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 L&S 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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Cory Hall Engineering

Physical

EE Devicesand Circuits

Systems

What happens to

faculty who work

at the intersections?

EE Signalsand Systems

Electrical

Engineering

Computer

Science

Computer

Science

EE/CS

Soda Hall

Departmental Structure


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EE Engineering

Signal Processing: 4.5

Communication: 3.0

Networks: 2.5

CAD: 3.5

ICs: 5.0

Solid State & MEM’s: 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

Faculty FTE Breakdown


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Agenda Engineering

  • The Information Age

  • EECS Department at Berkeley

  • Student Enrollment Pressures

  • Random Thoughts and Recommendations

  • Summary and Conclusions


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158 Engineering

142

286

243

UG Degree History at Berkeley

#Degrees

About

half are

CS degrees

Year


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The trend towards CS enrollment growth continues Engineering

Undergraduate Enrollment Trends

Total

EECS/EE

CS Total

EECS/CS

L&S CS


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A New Vision for EECS Engineering

“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 Engineering

EE

physics

circuits

signals

control

CS

algorithms

programming

comp systems

AI

Physical

World

Synthetic

World


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New View of EECS Engineering

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 Engineering

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 Engineering

  • 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 Engineering

  • 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 Engineering

  • 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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Sets Engineering

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

Topics Covered


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EE 40: Introduction to Microelectronics Circuits Engineering

  • 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 Programs

  • 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 Programs

  • 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 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 Programs

  • The Information Age

  • EECS Department at Berkeley

  • Student Enrollment Pressures

  • Random Thoughts and Recommendations

  • Summary and Conclusions


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Human Centered Systems Programs

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

Department’s Strategic Plan


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21st Century Challenge for Computer Science Programs

  • 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 Programs

  • 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 ProgramsEE 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: Affymetrix Programswww.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? Programs

  • 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 Programs

  • The Information Age

  • EECS Department at Berkeley

  • Student Enrollment Pressures

  • Random Thoughts and Recommendations

  • Summary and Conclusions


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Summary and Conclusions Programs

  • 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