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 at Pasadena City College EE1301: Intro. to Computer Science

3. 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. 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?) Vertical Slice of Computer Engineering

6. 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 Vertical Slice of Computer Engineering

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-Turing Thesis

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

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

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

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

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

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

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

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

23. All (or mostly) About “Bits” 01 zero one false true off on open closed not asserted asserted not set set … …

24. Truth Tables Example 8 rows 3 variables 4 rows 2 variables 2 m rows m variables 2 64 rows 64 variables 1111 0 0 0 0 1 1 1 0 0 1 1 0 0 1 0 1 0 1 0 1 0 0 0 0 1 0 1 0 x1x1 x2x2 x3x3 f

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

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

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

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

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

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

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

34. Linear Threshold Gates Useful Model?

35. inputsoutputs Digital Circuit circuit

36. inputsoutputs circuit gate Digital Circuit

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

38. Data Structures Example Truth Tables 1111 0 0 0 0 1 1 1 0 0 1 1 0 0 1 0 1 0 1 0 1 0 0 0 0 1 0 1 0 x1x1 x2x2 x3x3 f x1x1 x2x2 x3x3 f

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

40. A Computing System…

41. “ 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 Astonishing Hypothesis “T hat the astonishing hypothesis is astonishing.” – Christophe Koch, 1995 The Astonishing Part:

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

43. Artificial Life 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 Going from reading genetic codes to writing them.

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 DNA: string of n nucleotides ( n ≈ 10 9 )... ACCGTTGAATGACG... Nucleotides: Amino acid: coded by a sequence of 3 nucleotides. Proteins: produced from a sequence of m amino acids ( m ≈ 10 3 ) called a “gene”.

46. Biochemical Reactions: how types of molecules combine. + + 2a2a b c Playing by the Rules

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

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

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

50. Multiplication pseudo-code biochemical code

51. Exponentiation biochemical code pseudo-code

52. Raising-to-a-Power pseudo-code biochemical code

53. Language as a Window into the way the Brain Works Steven Pinker, Harvard

54. Circuits & Computers as a Window into our Linguistic Brains Circuit Brain Conceives of circuits and computation by “applying” language. Lousy at all the tasks that the brain that designed it is good at (including language). ?