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Report
Review for Exam 1
 Chapters 1 through 3
CSE260-1-1
Chapter 1 Overview
 Information Representation
 Number Systems [binary, octal and
hexadecimal]
 Base Conversion
 Decimal Codes [BCD (binary coded
decimal)]
 Alphanumeric Codes
 Parity Bit
 Gray Codes
CSE260-1-2
1-2 Number Systems
 Positive radix, positional number systems
 Examples:
• Decimal (radix r =10)
Ex: 24.3 = 2x101 + 4x100+3x10-1
• Binary (radix r =2)
Digits (0-9)
Ex: 1101.01 = ( . )10
• Octal (radix r = )
• Hexadecimal (r =
Bits (0-1)
)
Digits: 1,2,…9, A, B, C, D, E, F
CSE260-1-3
Range of numbers
 Binary number: ex. a 3-bit number: n=3
• 000, 001 … ,111
or in decimal system: 0, 1 … 7
 Total of 8 numbers (=23)
 Range: from 0 to 7 (0 to 23-1)
• In general a n-bit number represents:
 2n different numbers
 Min: 0
 Max number: 2n-1
 For fractions: m bits after the radix point:
• Min: 0
• Max number:
(2m -1)/2m
CSE260-1-4
Use of HEX system
 Short hand notation of large binary numbers:
• Each HEX digits can be represented by exactly 4 bits
•
(16=24)
Thus (10011110.0101)2
9
E .
5
Conversion from binary to HEX and HEX to binary is
very easy:
(10011101)2 = (
)16
(1010110110.11)2 = (
B39.716 = (
)2
)16
CSE260-1-5
Octal system
 Radix r = 8
 8 digits:
• 0, 1, 2,…7
 Ex: 2758 = 2x82 + 7x8 + 5x1 = 128 + 56 + 5
= 18910
 Each octal digit can be represented by 3 bits
CSE260-1-6
1-3 Conversion Between Bases
To convert from one base to another:
1) Convert the Integer Part
2) Convert the Fraction Part
3) Join the two results with a radix point
CSE260-1-7
Example: convert (325.65)10 to hex
 Integer part: 32510 = (
325/16 = 20 and rem = 5
20/16 = 1 and rem = 4
1/16 = 0 and rem = 1
 Fractional part: .65
. )16
Least significant digit
Most significant
Thus 32510 = 14516
0.65x16 = 10.4 thus int = 10= A Most significant
0.4x16 = 6.4 thus int = 6
0.4x16 = 6.4 thus int = 6
Least significant
Etc.
Thus .6510 = A6616
325.6510 = 145.A6616
CSE260-1-8
Conversion - Summary
Divisions (or x) by 16
Ai.16i
Decimal
SAi.2i
Binary
 Ai.8i
Group in
bits of 3
Hexadecimal
Group in
bits of 4
Octal Hex:
through the binary
representation
Octal
CSE260-1-9
1-4 Binary Codes
 A n-bit binary code is a n-bit word
which can represent up to 2n
different elements.
 Example: 3-bit code can represent
up to 8 different elements”
CSE260-1-10
Binary Coded Decimal (BCD)
 The BCD code is the 8,4,2,1 code.
 This code only encodes the first ten
values from 0 to 9.
 Each decimal digit is coded
separately by 4 bits
 Example:
• (325)10 = (0011 0010 0101)BCD
3
2
 Exercise: (856)10 = (
5
)BCD
CSE260-1-11
Overview Chapter 2
•
•
•
•
•
•
•
•
Binary Logic and Gates
Boolean Algebra
Standard Forms
Two-Level Optimization
Map Manipulation
Other Gate Types
Exclusive-OR Operator and Gates
High-Impedance Outputs
12
Operator Definitions and Truth
Tables
 Truth table - a tabular listing of the values of a
function for all possible combinations of values
on its arguments
 Example: Truth tables for the basic logic
operations:
X
0
0
1
1
AND
Y Z = X·Y
0
0
1
0
0
0
1
1
X
0
0
1
1
Y
0
1
0
1
OR
Z = X+Y
0
1
1
1
NOT
X
0
1
Z=X
1
0
13
2-2 Boolean Algebra

Boolean algebra deals with binary variables and
a set of three basic logic operations: AND (.), OR
(+) and NOT ( ) that satisfy basic identities
Basic identities
1.
3.
5.
7.
9.
X + 0= X
X+ 1=1
X + X= X
X + X=1
X = X Involution
2.
4.
6.
8.
X. 1=X
X . 0=0
X .X =X
X .X =0
Dual
Existence 0 and 1 or
operations with 0 and 1
Idempotence
Existence complements
Replace “+” by “.”, “.” by +,
“0” by “1” and “1’’ by”0”
14
Boolean Algebra
Boolean Theorems of multiple variables
10. X + Y =Y + X
14. X (Y+Z) = XY+XZ
11. XY =YX
13. (XY)Z =X(YZ )
Associative
Distributive 15. X+ YZ = (X + Y)(X + Z)
16. X + Y = X . Y
DeMorgan’s
12. (X + Y) + Z = X + (Y+ Z)
Commutative
17. X . Y = X + Y
Dual
15
Other useful Theorems




Dual
(X + Y)(X + Y) = Y
XY + XY = Y
Minimization
X + XY = X
Absorption
X(X + Y) = X
X + XY = X + Y
Simplification
X(X + Y) = XY
XY + XZ + YZ = XY + XZ
Consensus
(X + Y)( X + Z)(Y + Z) = (X + Y)( X + Z)
16
2-3 Standard (Canonical) Forms
 It is useful to specify Boolean
functions in a form that:
• Allows comparison for equality.
• Has a correspondence to the truth
tables
 Canonical Forms in common usage:
• Sum of Products (SOP), also called Sum
or Minterms (SOM)
• Product of Sum (POS), also called
Product of Maxterms (POM)
17
Maxterms and Minterms
 Examples: Two variable minterms and
maxterms.
Index
Minterm
Maxterm
0 (00)
xy
x+y
1 (01)
xy
x+y
2 (10)
xy
x+y
3 (11)
xy
x+y
 The index above is important for describing
which variables in the terms are true and which
are complemented.
18
Index Examples – Four Variables
Minterm Maxterm
mi
Mi
abcd a  b  c  d
?
abcd
?
abcd
abcd a  b  c  d
?
abcd
abcd a  b  c  d
?
abcd
abcd a  b  c  d
Notice: the variables
are in alphabetical
order in a standard
form
Index Binary
i Pattern
0
0000
1
0001
3
0011
5
0101
7
0111
10
1010
13
1101
15
1111
Relationship between min and MAX term?
M i = mi
mi = M i
19
Minterm Function Example
 F(A, B, C, D, E) = m2 + m9 + m17 + m23
 F(A, B, C, D, E) write in standard form:
A’B’C’DE’ + A’BC’D’E + AB’C’D’E + AB’CDE
m2
m9
m17
m23
 Sum of Product (SOP) expression:
• F = Σm(2, 9, 17, 23)
20
Expressing a function with
Maxterms
 Start with the SOP: F1(x,y,z) =m1 + m4 + m7
 Thus its complement F1can be written as
• F1 = m0 +m2 +m3 + m5 + m6 (missing term of F1)
 Apply deMorgan’s theorem on F1:
• (F1 = (m0 +m2 +m3 + m5 + m6)
= m0.m2.m3.m5.m6
= M0.M2.M3.M5.M6
also called, Big M notation
= ΠM(0,2,3,5,6)
Thus the Product of Sum terms (POS):
F1 = (x + y + z) ·(x + y + z)·(x+ y + z)
·(x + y + z)·(x + y + z)
21
2-4 Circuit Optimization
 Goal: To obtain the simplest
implementation for a given function
 Optimization requires a cost criterion to
measure the simplicity of a circuit
 Distinct cost criteria we will use:
• Literal cost (L)
• Gate input cost (G)
• Gate input cost with NOTs (GN)
22
Literal Cost
 Literal – a variable or its complement
 Literal cost – the number of literal
appearances in a Boolean expression
corresponding to the logic circuit
diagram
 Examples (all the same function):
•
•
•
•
F = BD + AB’C + AC’D’
F = BD + AB’C + AB’D’ + ABC’
F = (A + B)(A + D)(B + C + D’)( B’ + C’ + D)
Which solution is best?
L=8
L=
L=
23
Karnaugh Maps (K-map)
 A K-map is a collection of squares
• Each square represents a minterm
• The collection of squares is a graphical representation
•
•
of a Boolean function
Adjacent squares differ in the value of one variable
Alternative algebraic expressions for the same function
are derived by recognizing patterns of squares
 The K-map can be viewed as
• A reorganized version of the truth table
• A topologically-warped Venn diagram as used to
visualize sets in algebra of sets
24
2-5 Map Manipulation:
Systematic Simplification
 A Prime Implicant is a product term obtained by
combining the maximum possible number of adjacent
squares in the map into a rectangle with the number of
squares a power of 2.
 A prime implicant is called an Essential Prime Implicant
if it is the only prime implicant that covers (includes)
one or more minterms.
 Prime Implicants and Essential Prime Implicants can be
determined by inspection of a K-Map.
25
Don't Cares in K-Maps
 Sometimes a function table or map contains entries
for which it is known:
• the input values for the minterm will never occur, or
• The output value for the minterm is not used
 In these cases, the output value need not be defined
 Instead, the output value is defined as a “don't care”
 By placing “don't cares” ( an “x” entry) in the function
table or map, the cost of the logic circuit may be
lowered.
 Example 1: A logic function having the binary codes
for the BCD digits as its inputs. Only the codes for 0
through 9 are used. The six codes, 1010 through 1111
never occur, so the output values for these codes are
“x” to represent “don’t cares.”
26
Other Gate Types: overview
A
A
B
A
B
NAND
NOR
A
B
A
B
A B
BUF
XOR XNOR
0 0
0
1
1
0
1
0 1
1 0
0
1
1
1
0
0
1
1
0
0
1 1
1
0
0
0
1
27
The Tri-State Buffer
Symbol
IN
OUT
EN
Truth Table
EN
0
1
1
IN
X
0
1
OUT
Hi-Z
0
1
 For the symbol and truth table,
IN is the data input, and EN,
the control input.
 For EN = 0, regardless of the
value on IN (denoted by X),
the output value is Hi-Z.
 For EN = 1, the output value
follows the input value.
 Variations:
• Data input, IN, can be inverted
• Control input, EN, can be inverted
by addition of “bubbles” to signals.
OUT= IN.EN
28
Logic and Computer Design Fundamentals
Chapter 3 – Combinational
Logic Design
Charles Kime & Thomas Kaminski
© 2008 Pearson Education, Inc.
(Hyperlinks are active in View Show mode)
NAND Mapping Algorithm
1. Replace ANDs and ORs:
.
.
.
.
.
.
.
.
.
.
.
.
2. Repeat the following pair of actions until there
is at most one inverter between :
a. A circuit input or driving NAND gate output, and
b. The attached NAND gate inputs.
.
.
.
.
.
.
NOR Mapping Algorithm
1. Replace ANDs and ORs:
.
.
.
.
.
.
.
.
.
.
.
.
2. Repeat the following pair of actions until there
is at most one inverter between :
a. A circuit input or driving NAND gate output, and
b. The attached NAND gate inputs.
.
.
.
.
.
.
Enabling Function
 Enabling permits an input signal to pass
through to an output
 Disabling blocks an input signal from passing
through to an output, replacing it with a fixed
value
 The value on the output when it is disable can
be Hi-Z (as for three-state buffers and
transmission gates), 0 , or 1 ENX
F
 When disabled, 0 output
 When disabled, 1 output
 See Enabling App in text
(a)
X
F
EN
(b)
3-7 Decoding
 A n-bit binary code can represent up to m=2n
elements:
encoding
m elements
n-bit binary code
(ex. 256 alpha-num. chars) decoding
(ex. 8-bit ASCII code)
 Decoding - the conversion of an n-bit input code
to an m-bit output code with
n ≤ m ≤ 2n such that each valid code word
produces a unique output code
n bits
A0
:
:
An-1
n-2n
decoder
D0
D1
:
:
Dm-1
m-elements
≤ 2n
2-to-4 Line Decoder circuit
A0
A1
D0 = A1 A0
D1 = A1 A0
D2 = A1 A0
D3 = A1 A0
Notice that the outputs of the decoder
correspond to the minterms: Di=mi
Decoder Expansion
 Larger decoders can be realized by
implementing each minterm using a single
AND gate:
• However for large decoders this requires
multiple input AND gates which is not always
feasible.
• Better to use a hierarchical approach: build
larger ones from smaller decoders.
 Approach:
• Output AND gates have only 2 inputs and
implement the minterms.
• The output AND gates are driven by two
decoders with their numbers of inputs either
equal or differing by 1.
Rule for building large decoders
 k-to-2k decoder:
• One needs 2k output AND gates
• If k can be divided by 2:
 use two k/2-to-2k/2 decoders
• If k cannot divided by 2:
 use a (k+1)/2 and
 use a (k-1)/2 decoder.
 Previous example: 3-to-8 decoder
(k=3):
• Use a 2-to-4 and a 1-to-2 decoder
Combinational Logic Implementation
- Decoder and OR Gates
 Implement m functions of n variables
with:
• Sum-of-minterms expressions
• One n-to-2n-line decoder
• m OR gates, one for each output
Example
 Design and implement a majority
function F(ABC) using a 3-to-8
decoder
Indicate MSB, LSB
ABC F
000 0
 Truth table:
0
 Minterms:
• F=Sm(3,5,6,7)
001
010
011
100
101
110
111
0
0
1
0
1
1
1
A
2
B
1
C
0
1
2
3
4
5
6
7
 Implementation using decoder:
F
Encoding
 Typically, an encoder converts a code
containing exactly one bit that is 1 to a binary
code corresponding to the position in which the
1 appears: ex. D1=1  output 0001
D0
D1
:
:
Dm-1
0
1
2
3
m-1
0
encoder
0
1
0
0
0
1
2
n-1
A0
:
:
An-1
1
0
0
0
 Examples: Octal-to-Binary encoder
 Other examples?
Priority Encoder
D0
0
1
D1
1
D2
2
?
0
D3
A1
A0
3
V
To
processor
 If more than one input value is 1, then the encoder
just designed does not work.
 An encoder that can accept all possible
combinations of input values and produce a
meaningful result is a priority encoder.
 Among the 1s that appear, it selects the most
significant input position (or the least significant
input position) containing a 1 and responds with
the corresponding binary code for that position.
3-9 Selecting (multiplexers)
 Selecting of data or information is a critical
function in digital systems and computers
 Circuits that perform selecting have:
• A set of n information inputs from which the selection is
made
• A set of k control (select) lines for making the selection
I0
• A single output
0
n ≤ 2k inputs
I1
I2
I3
1
2
3
:
:
In-1
n-1
OUT
k-1 .. 1 0
Sk-1..S1 S0
k select lines
4:1 MUX realization
 Expression for OUT
OUT = S1S0 I0+ S1S0 I1+ S1S0 I2+ S1S0 I3
m0
m1
m2
m3
S1 S0 OUT
0 0
I0
0 1
I1
1 0
I2
1 1
I3
2k-1
or OUT = Σ mi Ii
i=0
 Circuit implementation: SOP
• 4 AND gates (4 product terms)
• 2-to-4 line decoder (to generate the
minterms)
Exercise
 Build a 8:1 MUX using two 4:1 and one 2:1 muxes
I0
I1
I2
I3
I4
I5
I6
I7
0
1
2 4:1
3
1 0
0
S1
1
S0
0
1
2 4:1
3
1 0
S1
S0
OUT
S2
Ex: S2S1S0=110 : select I6
Multiplexer-based combinational circuits
realization- Approach 1
 A mux can be easily used to implement a
function defined by a truth table (lookup table)
 Indeed the output F of a mux is equal to:
2k-1
F = Σ mi Ii
Example
i=0
Give the input Ii the
value of 0 or 1
as shown in the truth table
0
1
1
0
0
1
2 4:1
3
1 0
A
B
F
A
0
0
1
1
B OUT =F
0
I0
0 m0
1
I1
1 m1
0
I2
1 m2
1
I3
0 m3
F= Σm(1,2)
Combinational Logic Implementation
- Multiplexer Approach 2
 Implement any m functions of n + 1
variables by using:
• An m-wide 2n-to-1-line multiplexer
 Design:
• Find the truth table for the functions.
• Based on the values of the first n variables,
separate the truth table rows into pairs
• For each pair and output, define a rudimentary
function of the final variable (0, 1, X, X )

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