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ELEC 2200-002 Digital Logic Circuits Fall 2008 Logic Minimization (Chapter 3) Vishwani D. Agrawal James J. Danaher Professor Department of Electrical and.

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Presentation on theme: "ELEC 2200-002 Digital Logic Circuits Fall 2008 Logic Minimization (Chapter 3) Vishwani D. Agrawal James J. Danaher Professor Department of Electrical and."— Presentation transcript:

1 ELEC Digital Logic Circuits Fall 2008 Logic Minimization (Chapter 3) Vishwani D. Agrawal James J. Danaher Professor Department of Electrical and Computer Engineering Auburn University, Auburn, AL Fall 2008, Oct ELEC Lecture 5 1

2 Understanding Minimization Logic function: Fall 2008, Oct ELEC Lecture 5 2 a b c d F AND OR NOT

3 Understanding Minimization Reducing products: Fall 2008, Oct ELEC Lecture 5 3 Distributivity Complementation Identity Complementation Distribitivity Consensus theorem Distributivity Complement, identity

4 Understanding Minimization Reduced SOP: Fall 2008, Oct ELEC Lecture 5 4 b d F AND OR NOT

5 Understanding Minimization Reducing literals: Fall 2008, Oct ELEC Lecture 5 5 b d F OR NOT Absorption theorem d b bd

6 Logic Minimization Generally means – –In SOP form: Minimize number of products (reduce gates) and Minimize literals (reduce gate inputs) – –In POS form: Minimize number of sums (reduce gates) and Minimize literals (reduce gate inputs) Fall 2008, Oct ELEC Lecture 5 6

7 Product or Implicant or Cube Any set of literals ANDed together. Minterm is a special case where all variables are present. It is the largest product. A minterm is also called a 0-implicant of 0- cube. A 1-implicant or 1-cube is a product with one variable eliminated: Obtained by combining two adjacent 0-cubes ABCD + ABC  D = ABC(D+  D) = ABC Fall 2008, Oct ELEC Lecture 5 7

8 Cubes (Implicants) of 4 Variables Fall 2008, Oct ELEC Lecture 5 8 A B C D Minterm or 0-implicant or 0-cube A  B  C  D 1-implicant or 1-cube ABC 1 1 What is this?

9 Growing Cubes, Reducing Products Fall 2008, Oct ELEC Lecture 5 9 A B C D implicant or 1-cube  AB C 1-implicant or 1-cube ABC implicant or 2-cube BC What is this?

10 Largest Cubes or Smallest Products Fall 2008, Oct ELEC Lecture 5 10 A B C D 1 1 What is this? 3-implicant or 3-cube B

11 Implication and Covering A larger cube covers a smaller cube if all minterms of the smaller cube are included in the larger cube. A smaller cube implies (or subsumes) a larger cube if all minterms of the smaller cube are included in the larger cube. Fall 2008, Oct ELEC Lecture 5 11

12 Implicants of a Function Minterms, products, cubes that imply the function. Fall 2008, Oct ELEC Lecture A B C D

13 Prime Implicant (PI) A cube or implicant of a function that cannot grow larger by expanding into other cubes. Fall 2008, Oct ELEC Lecture A B C D A B C D PI

14 Essential Prime Implicant (EPI) If among the minterms subsuming a prime implicant (PI), there is at least one minterm that is covered by this and only this PI, then the PI is called an essential prime implicant (EPI). Also called essential prime cube (EPC). Fall 2008, Oct ELEC Lecture A B C EPI Why not this?

15 Redundant Prime Implicant (RPI) If each minterm subsuming a prime implicant (PI) is also covered by other essential prime implicants, then that PI is called a redundant prime implicant (RPI). Also called redundant prime cube (RPC). Fall 2008, Oct ELEC Lecture A B C EPI RPI

16 Selective Prime Implicant (SPI) A prime implicant (PI) that is neither EPI nor RPI is called a selective prime implicant (SPI). Also called selective prime cube (SPC). SPIs occur in pairs. Fall 2008, Oct ELEC Lecture A B C EPI SPI

17 Minimum Sum of Products (MSOP) Identify all prime implicants (PI) by letting minterms and implicants to grow. Construct MSOP with PI only : Cover all minterms Use only essential prime implicants (EPI) Use no redundant prime implicant (RfroPI) Use cheaper selective prime implicants (SPI) A good heuristic – Choose EPI in ascending order, starting from 0-implicant, then 1-implicant, 2- implicant,... Fall 2008, Oct ELEC Lecture 5 17

18 Example: F=  m(1,3,4,5,8,9,13,15) Fall 2008, Oct ELEC Lecture A B C D MSOP: F =  A  B D +  A B  C + A B D + A  B  C RPI

19 Example: F=  m(1,3,5,7,8,10,12,13,14) Fall 2008, Oct ELEC Lecture A B C D MSOP: F =  A D + A  D + A B  C SPI

20 Functions with Don’t Care Minterms F(A,B,C) =  m(0,3,7) + d(4,5) Include don’t care minterms when beneficial. Fall 2008, Oct ELEC Lecture  11  A B C 1  11  A B C F = B C +  A  B  CF = B C +  B  C

21 Five-Variable Functions F(A,B,C,D,E) =  m(0,1,4,5,6,13,14,15,22,24,25,28,29,30,31) Fall 2008, Oct ELEC Lecture A = 0 B C D E A = 1 B C D E F =  A  B  D + A B  D + B C E + C D  E

22 Multiple-Output Minimization Fall 2008, Oct ELEC Lecture 5 22 InputsOutputs ABCDF1F

23 Individual Output Minimization Need five products. Fall 2008, Oct ELEC Lecture 5 23 F1 A B C D F2 A B C D

24 Global Minimization Need four products. Fall 2008, Oct ELEC Lecture 5 24 F1 A B C D F2 A B C D

25 Minimized SOP and POS F(A,B,C,D) =  m(1,3,4,7,11) + d(5,12,13,14,15) =  M(0,2,6,8,9,10) D(5,12,13,14,15) Fall 2008, Oct ELEC Lecture   13      13    10 A B C D A B C D F = B  C +  AD + C D  F =  B  D + C  D + A  C F =(B + D)(  C + D)(  A + C)

26 SOP and POS Circuits F(A,B,C,D) =  m(1,3,4,7,11)+d(5,12,13,14,15) =  M(0,2,6,8,9,10) D(5,12,13,14,15) Fall 2008, Oct ELEC Lecture 5 26 F = B  C +  AD + C DF = (B + D)(  C + D)(  A + C) A B C D F A B C D F Are two circuits functionally Identical?

27 Absorption Theorem For two Boolean variables: A, B A + A B = A Proof: Fall 2008, Oct ELEC Lecture 5 27 B A AB

28 Consensus Theorem For three Boolean variables: A, B, C A B +  A C + B C = A B +  A C Proof: Fall 2008, Oct ELEC Lecture 5 28 B A C AB  AC BC

29 Growing Implicants to PI F = AB +  CD +  ABCDinitial implicants =AB +  ABCD + BCD +  CDconsensus th. =AB + BCD +  CDabsorption th. =AB + BCD +  CD + BDconsensus th. =AB +  CD + BD absorption th. Fall 2008, Oct ELEC Lecture 5 29 A B C D A B C D A B C D

30 Identifying EPI Find all prime implicants. From primary implicant SOP, remove a PI. Apply consensus theorem to the remaining SOP. If the removed PI is generated, then it is either an RPI or an SPI. If the removed PI is not generated, then it is an EPI Fall 2008, Oct ELEC Lecture 5 30

31 Example PI SOP: F = A D +  A  C +  CD Is AD an EPI? F – {AD}=  A  C +  CD, no new PI can be generated Hence, AD is an EPI. Fall 2008, Oct ELEC Lecture 5 31 A B C D AD

32 Example (Cont.) PI SOP: F = A D +  A  C +  CD Is  CD an EPI? F – {  CD } = A D +  A  C = A D +  A  C +  C D(Consensus th.) Hence  C D is not an EPI (it is an RPI) Fall 2008, Oct ELEC Lecture 5 32 A B C D  CD

33 Finding MSOP Start with minterm or cube SOP representation of Boolean function Find all prime implicants (PI) Include all EPI’s in MSOP Find the set of uncovered minterms, {UC} MSOP is minimum if {UC} is empty. DONE For a minterm in {UC}, include the largest PI from remaining PI’s (non-EPI’s) in MSOP Go to step 4. Fall 2008, Oct ELEC Lecture 5 33

34 Finding Uncovered Minterms, {UC} {UC} = ({PI} # {PMSOP}) # {DC} Where: {PI} is set of all prime implicants of the function. {PMSOP} is any partial SOP. {DC} is set of don’t care minterms. Sharp (#) operation between Boolean expressions X and Y, X # Y, is the set of minterms covered by X that are not covered by Y. Fall 2008, Oct ELEC Lecture 5 34

35 Example: # Operation AD #  CD = {A B C D, A  B C D}  CD # AD = {  A B  C D,  A B  C D} Fall 2008, Oct ELEC Lecture 5 35 A B C D AD  CD

36 Minterms Covered by a Product A product from which k variables have been eliminated, covers 2 k minterms. Example: For four variables, A, B, C, D Product AC covers 2 2 = 4 minterms: 1) 1) A  B C  D 2) 2) A  B C D 3) 3) A B C  D 4) 4) A B C D Obtained by inserting the eliminated variables in all possible ways. Fall 2008, Oct ELEC Lecture 5 36 (4)(2) (3)(1) A B C D

37 Quine-McCluskey Fall 2008, Oct Agrawal: ELEC Lecture 5 37 Willard V. O. Quine 1908 – 2000 Edward J. McCluskey b. 1929

38 Quine-McCluskey Tabular Minimization Method W. V. Quine, “The Problem of Simplifying Truth Functions,” American Mathematical Monthly, vol. 59, no. 10, pp , October E. J. McCluskey, “Minimization of Boolean Functions,” Bell System Technical Journal, vol. 35, no. 11, pp , November Textbook, Section 3.9, pp Fall 2008, Oct ELEC Lecture 5 38

39 Q-M Tabular Minimization Minimizes functions with many variables. Begin with minterms: Step 1: Tabulate minterms in groups of increasing number of true variables. Step 2: Conduct linear searches to identify all prime implicants (PI). Step 3: Tabulate PI’s vs. minterms to identify EPI’s. Step 4: Tabulate non-essential PI’s vs. minterms not covered by EPI’s. Select minimum number of PI’s to cover all minterms. Fall 2008, Oct ELEC Lecture 5 39

40 F(A,B,C,D) =  m(2,4,6,8,9,10,12,13,15) Q-M Step 1: Group minterms with 1 true variable, 2 true variables, etc. Fall 2008, Oct ELEC Lecture 5 40 MintermABCDGroups : single : two 1’s : three 1’s : four 1’s

41 Q-M Step 2 Find all implicants by combining minterms, and then products that differ in one variable: For example, 2 and 6, or  A  B C  D and  A B C  D →  A C  D, written as 0 – 1 0. Try combining a minterm (or product) with all minterms (or products) listed below. Include resulting products in the next list. If minterm (or product) does not combine with any other, mark it as PI. Check the minterm (or product) and repeat for all other minterms (or products). Fall 2008, Oct ELEC Lecture 5 41

42 Step 2 Executed on Example Fall 2008, Oct ELEC Lecture 5 42 List 1List 2List 3 MintermABCD(X)MintermsABCD(X)MintermsABCD(X) 20010X2,60-10PI28,9,12,131-0-PI X2,10-010PI X4,601-0PI X4,12-100PI X8,9100-X X8,1010-0PI X8,121-00X X9,131-01X X12,13110-X 13,1511-1PI7

43 Step 3 Identify EPI Covered by EPI →xxxxx Minterm → PI1xxxx PI2xx PI3xx PI4xx PI5xx PI6xx PI7xx Fall 2008, Oct ELEC Lecture 5 43

44 Step 4 Covering Remaining Minterms Remaining minterm →24610 PI2xx PI3xx PI4xx PI5x PI6x Fall 2008, Oct ELEC Lecture 5 44 Integer linear program (ILP), available from Matlab and other sources: Define integer {0,1} variables, xk = 1, select PIk; xk = 0, do not select PIk. Minimize  k xk, subject to following constraints: x2 + x3 ≥ 1 x4 + x5 ≥ 1 x2 + x4 ≥ 1 x3 + x6 ≥ 1 A solution is x3 = x4 = 1, x2 = x5 = x6 = 0

45 Linear Programming (LP) A mathematical optimization method for problems where some “cost” depends on a large number of input variables. An easy to understand introduction is: S. I. Gass, An Illustrated Guide to Linear Programming, New York: Dover Publications, Very useful tool for a variety of engineering design problems. Available in software packages like Matlab. Courses available in Math, Business and Engineering departments. Fall 2008, Oct ELEC Lecture 5 45

46 Q-M Solution and Verification   F(A,B,C,D) =PI1 + PI3 + PI4 + PI7 = =A  C +  B C  D +  A B  D + A B D   See Karnaugh map. Fall 2008, Oct ELEC Lecture A B C D

47 Further Reading Incompletely specified functions: See Example 3.25, pages Multiple outputs: See Example 3.26, pages Fall 2008, Oct ELEC Lecture 5 47


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