Part I: Machine Architecture

1. Part I: Machine Architecture • A major process in the development of a science is the construction of theories that are confirmed or rejected by experimentation. • In some cases these theories lie dormant for extended periods, waiting for technology to develop to the point that they can be tested. • In other cases the capabilities of current technology influence the concerns of the science.

2. Ch. 1 Data Storage • Storage of bits. • Main memory. • Mass storage. • Coding information for storage. • The binary system. • Storing integers. • Storing Fractions. • Communication errors.

3. Storage of bits • Today’s computers represent information as patterns of bits. • Gates are devices that produce the output of a Boolean operation when given the operation’s input values. • A flip-flop is a circuit that has one of two output values (i.e., 0 or 1), the output will flip or flop between two values under control of external stimuli.

4. Storage of Bits • A flip-flop is ideal for the storage of a bit within a computer (Fig 1.3 and 1.4). A flip-flop loses data when its power is turned off. • Cores, a donut-shaped rings of magnetic material, are obsolete today due to their size and power requirements. • A magnetic or laser storage device is commonly used when longevity is important. • Hexadecimal notation (Fig. 1.6).

5. Main Memory • Cells - a typical cell size is 8 or called byte. • MB = 1,048,576 (2 ** 20) bytes, KB and GB. • Address is used to identify individual cells in a main memory. • Random access memory (RAM). • Read only memory (ROM). • Most significant bit (MSB) and least significant bit (LSB).

6. Mass Storage • Secondary memory. • Storing large units of data (called files). • Mass storage systems are slow due to mechanical motion requirement. • On-line Vs. off-line operations.

7. Mass Storage • Mass StorageDisk storage. • Floppy disk and hard disk • Track, sector, seek time, latency time (rotation delay), access time, transfer time • Milliseconds Vs. nanoseconds • Compact disks and CD-ROM. • A single spiral track • Tape storage.

8. Mass Storage • Physical Vs. logical records. • Buffer. • Main memory and mass storage. • Main memory, magnetic disk, compact disk and magnetic tape exhibit decreasing degrees of random access to data.

9. Representing Text • American Standard Code for Information Interchange (ASCII) - 8-bit codes. • Appendix A • Figure 1.12 • Unicode - 16-bit codes; allow to represent most common Chinese and Japanese symbols. • International Standards Organization (ISO) - 32-bit codes.

10. Representing Numeric Values • Using 16 bits, the largest number we can store in ASCII is - • Binary notation (Figures 1.14 and 1.16). • Given 16 bits, the largest number we can store is - • A particular value may be represented by several different bit patterns; a particular bit pattern may be given several interpretations.

11. Representing Images • Bit map representation • An image is considered as a collection of pixel • a pixel can be black or white, represented by a bit • a pixel can be a color, represented by three bytes • A typical photograph consists of 1280 rows of 1024 pixels • requires several megabytes of storage • image compression • Vector representation provides a means of scaling.

12. The Binary System • Binary addition. • Fractions in binary. • Radix point (same as decimal point in decimal notation) • Figure 1.18 • Example of addition

13. Storing Integers in Computers • Two’s complement notation. • Figure 1.19 • Sign bit • How to decode a bit pattern? • Addition in two’s complement notation. • Addition of any combination of signed numbers can be accomplished using the same algorithm • simplify circuit design • Figure 1.21

14. Storing Integers in Computers • Overflow problem. • Limit to the size of the values that can be represented • 5 + 4 = -7 • Addition of two positive (negative) values appears to be negative (positive) • Excess notation. • Figures 1.22 and 1.23 • excess 8 (4) notation for bit patterns of length 4 (3)

15. Storing Fractions in Computers • Floating-point notation. • Sign bit, exponent field, mantissa field • Exponent expressed in excess notation • 01101011 = - • 1.125 = - • 0.375 = - • All nonzero values have a mantissa starting with 1

16. Storing Fractions in Computers • Round-off errors. • Mantissa field is not large enough • 2.625 = - • Order of computation • 2.5 + 0.125 + 0.125 = - • Nonterminating representation • 0.1 = - • change the unit of measure from dollar to cent for a dime

17. Data Compression • Run-length encoding. • A bit pattern consists of 253 1’s, followed by 118 0’s • Relative encoding. • Each data block is coded in terms of its relationship to the previous block

18. Data Compression • Frequency-dependent encoding. • More frequently used characters are represented by shorter bit patterns • Huffman codes • Adaptive dictionary encoding. • Lempel-Ziv encoding • ABAABQB (5,4,A) (0,0,D) (8,6,B)

19. Data Compression • GIF. • Each pixel is represented by a single byte • JPEG. • Human eyes are more sensitive to changes in brightness than color • Each four-pixel block is represented by six values rather than 12 values • MPEG.

20. Communication Errors • How can you make sure the information you received is correct? • Coding techniques for error detection and correction. • Parity bits. • Error-correcting codes. • Figures 1.28 and 1.29 • Hamming distance of at least five is able to detect up to - errors and correct up to - errors