Chapter 3. Minimization of Switching Functions. Given a sw function f(x 1, x 2, …, x n ) and some cost criteria, find a representation of f which minimizes.

Slides:



Advertisements
Similar presentations
FUNCTION OPTIMIZATION Switching Function Representations can be Classified in Terms of Levels Number of Levels, k, is Number of Unique Boolean (binary)
Advertisements

Combinational Logic Circuits Chapter 2 Mano and Kime.
ECE C03 Lecture 31 Lecture 3 Two-Level Logic Minimization Algorithms Hai Zhou ECE 303 Advanced Digital Design Spring 2002.
ECE 3110: Introduction to Digital Systems Simplifying Sum of Products using Karnaugh Maps.
Quine-McCluskey (Tabular) Minimization  Two step process utilizing tabular listings to:  Identify prime implicants (implicant tables)  Identify minimal.
Quine-McClusky Minimization Method Module M4.3 Section 5.3.
Chapter 3 Simplification of Switching Functions
SYEN 3330 Digital SystemsJung H. Kim Chapter SYEN 3330 Digital Systems Chapter 2 -Part 8.
ENEE 6441 On Quine-McCluskey Method > Goal: find a minimum SOP form > Why We Need to Find all PIs? f(w,x,y,z) = x’y’ +wxy+x’yz’+wy’z = x’y’+x’z’+wxy+wy’z.
1 Section 10.1 Boolean Functions. 2 Computers & Boolean Algebra Circuits in computers have inputs whose values are either 0 or 1 Mathematician George.
Quine-McClusky Minimization Method Discussion D3.2.
Computer Engineering (Logic Circuits) (Karnaugh Map)
Lecture 3. Boolean Algebra, Logic Gates
SYEN 3330 Digital Systems Jung H. Kim Chapter SYEN 3330 Digital Systems Chapter 2 – Part 5.
EECC341 - Shaaban #1 Lec # 7 Winter Combinational Circuit Minimization Canonical sum and product logic expressions do not provide a circuit.
Simplifying Boolean Expressions Using K-Map Method
Computer Architecture I: Digital Design Dr. Robert D. Kent Lecture 3 Simplification of Boolean Expressions.
Two Level Logic Optimization. Two-Level Logic Minimization PLA Implementation Ex: F 0 = A + B’C’ F 1 = AC’ + AB F 2 = B’C’ + AB product term AB, AC’,
Chapter 2: Boolean Algebra and Logic Functions
BOOLEAN FUNCTION PROPERTIES
Combinatorial Algorithms Unate Covering Binate Covering Graph Coloring Maximum Clique.
The covering procedure. Remove rows with essential PI’s and any columns with x’s in those rows.
Quine-McCluskey (Tabular) Minimization Two step process utilizing tabular listings to: Identify prime implicants (implicant tables) Identify minimal PI.
Two-Level Simplification Approaches Algebraic Simplification: - algorithm/systematic procedure is not always possible - No method for knowing when the.
ECE 2110: Introduction to Digital Systems PoS minimization Don’t care conditions.
Chapter 6. Threshold Logic. Logic design of sw functions constructed of electronic gates different type of switching element : threshold element. Threshold.
ECE 3110: Introduction to Digital Systems Symplifying Products of sums using Karnaugh Maps.
UM EECS 270 Spring 2011 – Taken from Dr.Karem Sakallah Logic Synthesis: From Specs to Circuits Implementation Styles –Random –Regular Optimization Criteria.
Two Level Networks. Two-Level Networks Slide 2 SOPs A function has, in general many SOPs Functions can be simplified using Boolean algebra Compare the.
Copyright © 2004 by Miguel A. Marin Revised McGILL UNIVERSITY DEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING COURSE ECSE DIGITAL SYSTEMS.
Circuit Minimization. It is often uneconomical to realize a logic directly from the first logic expression that pops into your head. Canonical sum and.
PRASAD A. PAWASKAR SPN. NO DETE 2 SEMESTER lec1-11.
Boolean Algebra Introduction Logical arguments are valid (or not) by virtue of their form, not content Example All men are mortal(premise 1) Harry is a.
Gate-Level Minimization
Chapter 2 Two- Level Combinational Logic. Chapter Overview Logic Functions and Switches Not, AND, OR, NAND, NOR, XOR, XNOR Gate Logic Laws and Theorems.
Chapter3: Gate-Level Minimization Part 1 Origionally By Reham S. Al-Majed Imam Muhammad Bin Saud University.
CHAPTER 3: PRINCIPLES OF COMBINATIONAL LOGIC
Lecture 4 Nand, Nor Gates, CS147 Circuit Minimization and
Ahmad Almulhem, KFUPM 2010 COE 202: Digital Logic Design Combinational Logic Part 3 Dr. Ahmad Almulhem ahmadsm AT kfupm Phone: Office:
Discrete Mathematics CS 2610 September Equal Boolean Functions Two Boolean functions F and G of degree n are equal iff for all (x 1,..x n )  B.
ECE/CS 352 Digital System Fundamentals1 ECE/CS 352 Digital Systems Fundamentals Spring 2001 Chapter 2 – Part 5 Tom Kaminski & Charles R. Kime.
Digital Logic (Karnaugh Map). Karnaugh Maps Karnaugh maps (K-maps) are graphical representations of boolean functions. One map cell corresponds to a row.
1 Digital Design Debdeep Mukhopadhyay Associate Professor Dept of Computer Science and Engineering NYU Shanghai and IIT Kharagpur.
©2010 Cengage Learning SLIDES FOR CHAPTER 6 QUINE-McCLUSKEY METHOD Click the mouse to move to the next page. Use the ESC key to exit this chapter. This.
Technical Seminar II Implementation of
Chapter 3 The Karnough Map Minimum Forms of Switching Functions 1.Combine terms by using ( Adjacency) Do this repeatedly to eliminates as.
CHAPTER 6 Quine-McCluskey Method
CHAPTER 3 Simplification of Boolean Functions
Lecture 4 Nand, Nor Gates, CS147 Circuit Minimization and
Chapter 2: Boolean Algebra and Logic Functions
3-7 Other Two-level Implementations
Lecture #6 EGR 277 – Digital Logic
CS 105 Digital Logic Design
QUINE-McCLUSKEY METHOD
CS 352 Introduction to Logic Design
ECE 2110: Introduction to Digital Systems
Boolean Algebra.
Lecture 3 Boolean algebra
Lecture 4 Sums of Product Circuits Simplification
ECE 331 – Digital System Design
CHAPTER 5 KARNAUGH MAPS 5.1 Minimum Forms of Switching Functions
Chapter 3 Gate-level Minimization.
Quine-McClusky Minimization Method
MINTERMS and MAXTERMS Week 3
SYEN 3330 Digital Systems Chapter 2 – Part 6 SYEN 3330 Digital Systems.
COE 202: Digital Logic Design Combinational Logic Part 3
Minimization of Switching Functions
Overview Part 2 – Circuit Optimization
CHAPTER 6 QUINE-McCLUSKEY METHOD
ECE 331 – Digital System Design
Presentation transcript:

Chapter 3. Minimization of Switching Functions

Given a sw function f(x 1, x 2, …, x n ) and some cost criteria, find a representation of f which minimizes the given cost criteria. Cost criteria : minimize the number of terms and literals per term. Literal : complemented or uncomplemented appearance of var. Term : sum of product of literal. Minimal : minimum number of terms with the minimum number of literals. Minimization method 1) algebraic method : f(x,y,z) = xyz + xyz + xyz + xyz + xyz + xyz = xz + yz + yz + xz 2) Map method adjacent state ( only single var. diff.) xy z

o x ) Tabular Method (Quine-McCluskey method) A sum of product expression for a function f is oReducible : if sum of the product terms can be deleted without changing the function realized. oIrreducible : not reducible. oMinimal : minimize the cost criteria. irreducible minimal f(x 1,x 2,x 3 ) =  (0,1,2,3,4) For an n variables, consider all true vertices for which one particular position of the n - tuple is constant at value  for  = 0 or 1. These true vertices from an (n-1) cube. The set of all true vertices constant in m position forms (n-m) cube. Consider f(x 1,x 2,…,x n ) on the n cube, then for an r cube(r<n) at the n-cube, which corresponds entirely to true vertices of n-cube, say the r-cube defined by x i-1 =a 1, x i-2 =a 2, …, x i n-r =a n-r Then, the product term x * i-1 x * i-2 … x * i n-r,where x * ij = x ij, if a j =1 is called an implicant of f. x ’ ij, if a j =0

An implicant is a prime implicant (P.I.) iff it is not covered by any other implicant of the function. Eg) previous vertices, implicant x 2 ’ x 3 ’ P.I.’s x 2 ’ x 3 ’ x 1 ’ x 3 ’ x 1 ’ x 1 ’ x 2 ’ x 1 ’ x 3 x 1 ’ x 2 x 1 ’ A P.I. is an essential P.I. iff itcovers some vertex not covered by any other prime implicant. Eg) f(x 1,x 2,x 3 ) =  (0,2,3,4,7) Implicant : x 2 ’ x 3 ’, x 1 ’ x 3 ’, x 1 ’ x 2, x 2 x 3 P.I. : x 2 ’ x 3 ’, x 1 ’ x 3 ’, x 1 ’ x 2, x 2 x 3 Essential P.I. : x 2 ’ x 3 ’, x 2 x 3 Any minimal sum of product expression contains only prime implicants, any minimal sum of product expression must contain all essential PI.

Procedure for minimal expression 1. Determine all essential PI’s and include them in the minimal expression. If all essential PI’s cover all minterms, then a unique minimal expression is found. 2. Remove from the list of PI all those minterms which are covered by essential PI’s. 3. Select additional PI so that f is covered completely and the total # and size of the PI’s added are minimal. Quine-McCluskey (tabular) method. 1. Arrange all minterms in group such that all terms in the same group have the same # of 1’s in their binary representation. 2. Compare every term of the lowest-index group with each term in the successive group. Whenever possible, combine two terms being compared by means of gx i +gx i ’ =g(x i +x i ’ )=g. Two terms from adjacent groups are combinable if their binary representation differ by just a single digit in the same position  (from all 1-cube). 3. The process continues until no further combinations are possible. The remaining unchecked terms constitute the set of PI.

Ex) f(x 1,x 2,x 3,x 4 ) =  (0,1,2,5,6,7,8,9,10,13,15) Using prime implicant chart, we can find essential PI (5,7) (5,13) (6,7) (9,13) (1,5) (1,9) (2,6) (2,10) (8,9) (8,10) (13,15) (7,15)                 (0,1,8,9) (0,2,8,10) (1,5,9,13) (5,7,13,15) (0,1) (0,2) (0,8) x 1,x 2,x 3,x 4 #         (2,6) (6,7) (0,1,8,9) (0,2,8,10) (1,5,9,13) (5,7,13,15)                

The essential PI’s are (0,2,8,10) and (5,7,13,15). So, f(x 1,x 2,x 3,x 4 ) = (0,2,7,8) + (5,7,13,15) + PI’s Here are 4 different choices (2,6) + (0,1,8,9), (2,6) + (1,5,9,13) (6,7) + (0,1,8,9), or (6,7) + (1,5,9,13) (2,6) (6,7) (0,1,8,9) (1,5,9,13)   The reduced PI chart A PI p j dominates PI p k iff every minterm covered by p k is also covered by p j. pjpkpjpk m 1 m 2 m 3 m 4      (can remove) Branching method p1p2p3p4p5p1p2p3p4p5 m 1 m 2 m 3 m 4 m 5           If we choose p 1 first, then p 3, p 5 are next. p1p1 p4p4 p3p3 p5p5 p3p3 p2p2 Quine – McCluskey method (no limitation of the # of variables)   

A set of logical primitives that can be used to realize any combinational function is called logically complete. Strongly complete : if any combinational function including the constant 0 and 1 can be realized by interconnecting a finite number of primitives from the set, assuming only the uncomplemented variables are available as input. Weakly complete : if the primitives together with constants 0 and 1 can realize any combinational function. Strongly complete : NAND, NOR, (AND, NOT) Weakly complete : ( ⊕ ) ring sum expression A function is zero-preserving if f(0,0,,0) = 0 A function is one-preserving if f(1,1,,1) = 1 The dual of a function f(x 1, x 2,, x n ) is the function f d obtained by interchanging all and + operation and all constants 0 and 1 which appear in the expression f d (x 1, x 2,, x n ) = f ’ (x 1 ’, x 2 ’,, x n ’ ) A function is self-dual iff f d (x 1, x 2,, x n ) = f(x 1, x 2,, x n ) Eg) f(x 1, x 2, x 3 ) = x 1 ’ x 2 ’ +x 1 ’ x 3 +x 2 ’ x 3 f d (x 1, x 2, x 3 ) = (x 1 ’+x 2 ’)(x 1 ’+x 3 )(x 2 ’+x 3 ) = (x 1 ’+x 1 ’x3+x 1 ’x 2 ’+x 2 ’x 3 )(x 2 ’+x 3 ) = (x 1 ’+x 2 ’x 3 )(x 2 ’+x 3 ) = x 1 ’x 2 ’+x 1 ’x 3 +x 2 ’x 3 +x 2 ’x 3 (self-dual)

A function f(x 1, x 2,, x n ) is positive-unate ( monotonic) iff f(a 1, a 2,, a n ) =1 implies f(b 1, b 2,, b n ) =1 for all (b 1, b 2,, b n )  (a 1, a 2,, a n )  negative unate f =  (2,3,6,7)  positive-unate if (001) = 1 then (011, 101, 111) also have 1  f =  (1,3,5,7)  positive-unate but f =  (1,5,7)  not positive-unate A function is linear iff its ringsum expression is of the form a 0 ⊕ a 1 x 1 ⊕ a 2 x 2 ⊕ ⊕ a n x n, where ai  {0,1} for