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CS 140 Lecture 3 Combinational Logic

CS 140 Lecture 3 Combinational Logic . Professor CK Cheng CSE Dept. UC San Diego. Part I Combinational Logic. Specification Implementation K-maps. Definitions. Literals x i or x i ’ Product Term x 2 x 1 ’x 0 Sum Term x 2 + x 1 ’ + x 0

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CS 140 Lecture 3 Combinational Logic

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  1. CS 140 Lecture 3Combinational Logic Professor CK Cheng CSE Dept. UC San Diego

  2. Part I Combinational Logic. • Specification • Implementation • K-maps

  3. Definitions Literals xi or xi’ Product Term x2x1’x0 Sum Term x2 + x1’ + x0 Minterm of n variables: A product of n literals in which every variable appears exactly once. Maxterm of n variables: A sum of n literals in which every variable appears exactly once.

  4. Implementation Specification  Schematic Diagram Net list, Switching expression Obj min cost  Search in solution space (max performance) Cost: wires, gates  Literals, product terms, sum terms We want to minimize # of terms, # of literals

  5. Implementation (Optimization) An example of 2-variable function f(A,B)

  6. Function can be represented by sum of minterms: f(A,B) = A’B+AB’+AB This is not optimal however! We want to minimize the number of literals and terms. We factor out common terms – A’B+AB’+AB= A’B+AB’+AB+AB =(A’+A)B+A(B’+B)=B+A Hence, we have f(A,B) = A+B

  7. K-Map: Truth Table in 2 Dimensions A = 0 A = 1 AB’ 0 2 0 1 1 1 B = 0 B = 1 1 3 A’B AB f(A,B) = A + B

  8. Another Example f(A,B)=A’B+AB=(A’+A)B=B

  9. On the K-map: A = 0 A= 1 0 2 0 0 1 1 B= 0 B = 1 1 3 AB A’B f(A,B)=B

  10. Using Maxterms f(A,B)=(A+B)(A’+B)=(AA’)+B=0+B=B

  11. Two Variable K-maps Id a b f (a, b) 0 0 0 f (0, 0) 1 0 1 f (0, 1) 2 1 0 f (1, 0) 3 1 1 f (1, 1) # possible 2-variable functions: For 2 variables as inputs, we have 4=22 entries. Each entry can be 0 or 1. Thus we have 16=24 possible functions. a b f(a,b)

  12. Two-Input Logic Gates

  13. More Two-Input Logic Gates

  14. Representation of k-Variable Func. (0,1,1,0) (0,1,1,1) (1,1,1,0) (1,1,1,1) • Boolean Expression • Truth Table • Cube • K Map • Binary Decision Diagram B (0,0,1,1) (0,0,1,0) (1,0,1,0) (1,0,1,1) C (0,1,0,1) (1,1,0,1) D (0,0,0,0) (0,0,0,1) (1,0,0,0) (1,0,0,1) A A cube of 4 variables: (A,B,C,D)

  15. Three-Variable K-Map Id a b c f (a,b,c) 0 0 0 0 1 1 0 0 1 0 2 0 1 0 1 3 0 1 1 0 4 1 0 0 1 5 1 0 1 0 6 1 1 0 1 7 1 1 1 0

  16. Corresponding K-map b = 1 Gray code (0,0) (0,1) (1,1) (1,0) 0 2 6 4 c = 0 1 1 1 1 1 3 7 5 c = 1 0 0 0 0 a = 1 f(a,b,c) = c’

  17. Karnaugh Maps (K-Maps) • Boolean expressions can be minimized by combining terms • K-maps minimize equations graphically

  18. K-map • Circle 1’s in adjacent squares • In the Boolean expression, include only the literals whose true y(A,B)=A’B’C’+A’B’C= A’B’(C’+C)=A’B’

  19. Another 3-Input example Id a b c f (a,b,c) 0 0 0 0 0 1 0 0 1 0 2 0 1 0 1 3 0 1 1 0 4 1 0 0 1 5 1 0 1 1 6 1 1 0 - 7 1 1 1 1

  20. Corresponding K-map b = 1 (0,0) (0,1) (1,1) (1,0) 0 2 6 4 c = 0 0 1 - 1 1 3 7 5 c = 1 0 0 1 1 a = 1 f(a,b,c) = a + bc’

  21. Yet another example Id a b c f (a,b,c,d) 0 0 0 0 1 1 0 0 1 1 2 0 1 0 - 3 0 1 1 0 4 1 0 0 1 5 1 0 1 1 6 1 1 0 0 7 1 1 1 0

  22. Corresponding K-map b = 1 (0,0) (0,1) (1,1) (1,0) 0 2 6 4 c = 0 1 - 0 1 1 3 7 5 c = 1 1 0 0 1 a = 1 f(a,b,c) = b’

  23. 4-input K-map

  24. 4-input K-map

  25. 4-input K-map

  26. K-maps with Don’t Cares

  27. K-maps with Don’t Cares

  28. K-maps with Don’t Cares

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