EE1301: Intro. to Computer Science

1. EE1301: Intro. to Computer Science • Browsing the “World Wide Web”with Microsoft Explorer™ • File management Microsoft XP Operating System™ • Writing documents with Microsoft Word™ • Preparing presentations with Microsoft Powerpoint™ • Operating on spreadsheets with Microsoft Excel™ • Reading and composing electronic mail,“e-mail,” with Microsoft Outlook™ The students will learn the fundamentals of computer science including:

2. CIS 106: Intro. to Computer Science EE1301: Intro. to Computer Science at Pasadena City College

3. EE1301: Intro. to Computer Science Computer Systems Computer Engineering

4. EE1301: Intro. to Computing Systems The course will introduce the fundamental concepts of computing systems from the machine level to high-level language programming, including: • transistors and logic circuits • binary arithmetic and data representation • memory and pointer addressing • data types and structures • Assembly Language • C programming

5. Vertical Slice of Computer Engineering • Quantum Physics (what’s an atom?) • Material Science (why does doped silicon behave as a semiconductor?) • Device Physics (how does a transistor work?) • Circuits(how do we put transistors together to get simple logic functions?) • Logic Design (how do we get complicated logic functions from simpler ones?) • Computer Architecture (how do we build a computer from logic functions?) • Assembly Programming (how do we specify tasks in the form of instructions for the computer?) • High-Level Programming (how do we specify tasks in a form that can be translated into instructions for the computer?)

6. Vertical Slice of Computer Engineering • Quantum Physics (what’s an atom?) • Material Science (why does doped silicon behave as a semiconductor?) • Device Physics (how does a transistor work?) • Circuits(how do we put transistors together to get simple logic functions?) • Logic Design (how do we get complicated logic functions from simpler ones?) • Computer Architecture (how do we build a computer from logic functions?) • Assembly Programming (how do we specify tasks in the form of instructions for the computer?) • High-Level Programming (how do we specify tasks in a form that can be translated into instructions for the computer?) EE1301 CS 1901 & CS1902

7. No Hamsters, No Magic Any sufficiently advanced technology is indistinguishable from magic. – Arthur C. Clarke

8. Examples of Computing Systems Are all these systems “equivalent”?

9. Examples of Computing Systems Are all these systems “ Turing Equivalent”?

10. Concepts vs. Jargon “Now this end is called the thagomizer, after the late Thag Simmons.”

11. Turing Machine

12. Turing Equivalence “It can be shown that a single special machine of that type can be made to do the work of all. It could in fact be made to work as a model of any other machine. The special machine may be called the universal machine.” – Alan Turing,1947 “The problems solvable by a universal Turing machine are exactly those problems solvable by an algorithm or an effective method of computation, for any reasonable definition of those terms. .” – Church-TuringThesis

13. Turing Universal Systems • Machine that can execute any C program. main(){ for(;;){ printf("Hello World!\n"); } }

14. Turing Universal Systems • Machine that can execute any Assembly program.

15. outputs inputs combinational circuit memory elements clock Turing Universal Systems • Synchronous Digital System

16. Building Digital Circuits Intel 4004(1971) ~2000 gates Intel “Nehalem”(2008) ~2 billion gates

17. Boxes inside Boxes [inside boxes…] 2000 transistors(Intel 4004, 1971) 800 million transistors(Intel Penryn, 2007) 1 transistor (1960’s)

18. From Chips to Computers IBM’s Blue Gene: 64,000 Processors

19. The Computational Landscape “There are known ‘knowns’; and there are unknown ‘unknowns’; but today I’ll speak of the known ‘unknowns’.” – Donald Rumsfeld, 2002 • Abutting true physical limits. • Cost and complexity are starting to overwhelm. Semiconductors:exponentially smaller, faster, cheaper – forever?

20. circuit Integrated Circuits inputs outputs 0 1 1 0 1 1 0 0 0 1 • What do integrated circuits do? • accept zeros and ones as inputs; • produce zeros and ones as outputs.

21. circuit Integrated Circuits inputs outputs 0 1 1 0 1 1 0 0 0 1 • Why do we want this? • zeros and ones represent information; • circuit performs computation.

22. circuit Integrated Circuits inputs outputs 0 1 1 0 1 1 0 0 0 1 • How do we build (design) such circuits? • hierarchically, from components.

23. All (or mostly) About “Bits” 0 1 one zero true false on off closed open asserted not asserted set not set … …

24. x1 x2 x3 f mvariables 2mrows 0 0 0 0 0 0 1 0 0 1 0 0 0 1 1 1 1 0 0 0 1 0 1 1 1 1 0 0 1 1 1 1 Truth Tables Example 2 variables 4 rows 3variables 8 rows 64variables 264rows

26. One made-up fact… [well, an abstraction really…] A Logic Gate

27. 0 0 0 1 1 0 1 1 Logic Gates Common Gate: “AND” gate 0 0 0 1

28. Logic Gates Common Gate: “OR” gate 0 0 0 0 1 1 1 0 1 1 1 1

29. Logic Gates Common Gate: “NAND” gate 0 0 1 0 1 1 1 0 1 1 1 0

30. Logic Gates Common Gate: “NOR” gate 0 0 1 0 1 0 1 0 0 1 1 0

31. Logic Gates Common Gate: “XOR” gate 0 0 0 0 1 1 1 0 1 1 1 0

33. w x 1 1 w x 2 2 w ... 0 w x n n Linear Threshold Gates

34. Linear Threshold Gates Useful Model?

35. inputs outputs circuit Digital Circuit

36. inputs outputs circuit gate Digital Circuit

37. NAND OR AND AND NOR AND Digital Circuit 1 1 0 1 0 0 0 1 0 1 1 1

38. x3 x1 x2 Data Structures Truth Tables Example x1 x2 x3 f 0 0 0 0 0 0 1 0 0 1 0 0 f 0 1 1 1 1 0 0 0 1 0 1 1 1 1 0 0 1 1 1 1

39. outputs inputs combinational circuit memory elements clock Sequential Circuits synchronous, finite number of states

40. A Computing System…

41. Astonishing Hypothesis The Astonishing Part: “A person's mental activities are entirely due to the behavior of nerve cells, glial cells, and the atoms, ions, and molecules that make them up and influence them.” – Francis Crick, 1982 “That the astonishing hypothesis is astonishing.” – Christophe Koch, 1995

42. Circuit Domains of Expertise • Vision • Language • Abstract Reasoning • Farming • Number Crunching • Mining Data • Iterative Calculations Human

43. Artificial Life Going from reading genetic codes to writing them. US Patent 20070122826 (pending):“The present invention relates to a minimal set of protein-coding genes which provides the information required for replication of a free-living organism in a rich bacterial culture medium.” – J. Craig Venter Institute

44. Artificial Life Going from reading genetic codes to writing them. Moderator: “Some people have accused you of playing God.” J. Craig Venter:“Oh no, we’re not playing.

45. Biochemistry in a Nutshell Nucleotides: DNA: string of n nucleotides (n ≈ 109) ... ACCGTTGAATGACG... Amino acid: coded by a sequence of 3 nucleotides. Proteins: produced from a sequence of m amino acids (m ≈ 103) called a “gene”.

46. + 2a c b + Playing by the Rules Biochemical Reactions: how types of molecules combine.

47. Biochemical Reactions + cell proteins count 9 8 6 5 7 9 Discrete chemical kinetics; spatial homogeneity.

48. Biochemical Reactions + + + Relative rates or (reaction propensities): slow medium fast Discrete chemical kinetics; spatial homogeneity.

49. Protein-ProteinChemistry y [computational]Biochemistry Biochemical[computation] x z quantity quantities

50. Multiplication pseudo-code biochemical code