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Berkeley COMPSCI 150 - Section 5 Simplification and State Minimization

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EECS150Section 5Simplification and State MinimizationFall 2001EECS150 - Fall 2001 1-2SimplificationFinding a minimal sum of products or product of sums realizationExploit don't care information in the processAlgebraic simplificationNot an algorithmic/systematic procedureHow do you know when the minimum realization has been found?Computer-aided design toolsPrecise solutions require very long computation times, especially for functions with many inputs (> 10)Heuristic methods employed – "educated guesses" to reduce amount of computation and yield good if not best solutionsHand methods still relevantTo understand automatic tools and their strengths and weaknessesAbility to check results (on small examples)EECS150 - Fall 2001 1-3ABF001010101110B has the same value in both on-set rows–B remainsA has a different value in the two rows– A is eliminatedF = A'B'+AB' = (A'+A)B' = B'The Uniting TheoremKey tool to simplification: A (B' + B) = AEssence of simplification of two-level logicFind two element subsets of the ON-set where only one variable changes its value – this single varying variable can be eliminated and a single product term used to represent both elementsEECS150 - Fall 2001 1-41-cubeX01Boolean cubesVisual technique for indentifying when the uniting theorem can be appliedn input variables = n-dimensional "cube"2-cubeXY110001103-cubeXYZ0001111014-cubeWXYZ0000011111111000EECS150 - Fall 2001 1-5ABF001010101110ON-set = solid nodesOFF-set = empty nodestwo faces of size 0 (nodes) combine into a face of size 1(line)A varies within face, B does notthis face represents the literal B'Mapping truth tables onto cubesUniting theorem combines two "faces" of a cube into a larger "face"Example:AB11000110FEECS150 - Fall 2001 1-6ABCin Cout000 0001 001 0 001 1 1100 0101 1110 1111 1Cout = BCin+AB+ACinA(B+B')Cinthe on-set is completely covered by the combination (OR) of the subcubes of lower dimensionality - note that “111”is covered three timesThree variable exampleBinary full-adder carry-out logicABC000111101(A'+A)BCinAB(Cin'+Cin)EECS150 - Fall 2001 1-7F(A,B,C) = Σm(4,5,6,7)A is asserted (true) and unchangedB and C varyHigher dimensional cubesSub-cubes of higher dimension than 2ABC000111101100001010011110on-set forms a squarei.e., a cube of dimension 2represents an expression in one variable i.e., 3 dimensions – 2 dimensionsThis subcube represents theliteral AEECS150 - Fall 2001 1-8m-dimensional cubesIn a 3-cube (three variables):0-cube, i.e., a single node, yields a term in 3 literals1-cube, i.e., a line of two nodes, yields a term in 2 literals2-cube, i.e., a plane of four nodes, yields a term in 1 literal3-cube, i.e., a cube of eight nodes, yields a constant term "1"In general,m-subcube within an n-cube (m < n) yields a term with n – m literalsEECS150 - Fall 2001 1-9ABF00101 0101110Karnaugh mapsFlat map of Boolean cubeWrap–around at edgesHard to draw and visualize for more than 4 dimensionsVirtually impossible for more than 6 dimensionsAlternative to truth-tables to help visualize adjacenciesGuide to applying the uniting theoremOn-set elements with only one variable changing value are adjacent unlike the situation in a linear truth-table021301AB011001EECS150 - Fall 2001 1-10Karnaugh maps (cont’d)Numbering scheme based on Gray–codee.g., 00, 01, 11, 10Only a single bit changes in code for adjacent map cells021300 01ABC01647511 10CBA02136475CBA041512 813 9DA372615 1114 10CB13 = 1101= ABC’DEECS150 - Fall 2001 1-11Adjacencies in Karnaugh mapsWrap from first to last columnWrap top row to bottom row000 010001 011110 100111 101CBAABC000111101100001010011110EECS150 - Fall 2001 1-12obtain thecomplementof the function by covering 0swith subcubesKarnaugh map examplesF =Cout =f(A,B,C) = Σm(0,4,6,7) 00011011CinBA1100BA10000111CBAB’ABAC+ ACin+BCin+ B’C’+ AB’EECS150 - Fall 2001 1-13F(A,B,C) = Σm(0,4,5,7)F'(A,B,C) = Σm(1,2,3,6)F' simply replace 1's with 0's and vice versaG(A,B,C) = More Karnaugh map examples00001111CBA10000111CBA01111000CBAA= AC + B’C’= BC’ + A’CEECS150 - Fall 2001 1-14C + B’D’find the smallest number of the largest possiblesubcubes to cover the ON-set(fewer terms with fewer inputs per term)Karnaugh map: 4-variable exampleF(A,B,C,D) = Σm(0,2,3,5,6,7,8,10,11,14,15)F =DABABCD00001111100001111001010011111111C+ A’BDEECS150 - Fall 2001 1-15+ B’C’DKarnaugh maps: don’t caresf(A,B,C,D) = Σ m(1,3,5,7,9) + d(6,12,13) without don't caresf = 0011X0X1DA110X0000BCA’DEECS150 - Fall 2001 1-16Karnaugh maps: don’t caresf(A,B,C,D) = Σ m(1,3,5,7,9) + d(6,12,13)f = A'D + B'C'D without don't caresf = with don't caresdon't cares can be treated as1s or 0sdepending on which is more advantageous0011X0X1DA110X0000BCby using don't care as a "1"a 2-cube can be formed rather than a 1-cube to coverthis nodeA'D + C'DEECS150 - Fall 2001 1-17we'll need a 4-variable Karnaugh map for each of the 3 output functionsExample: two-bit comparatorblock diagramLTEQGTA B < C DA B = C DA B > C DABCDN1N2ABCD LTEQGT0000 0 1 001 1 0 010 1 0 011 1 0 001 00 0 0 101 0 1 010 1 0 011 1 0 01 000 0 0 101 0 0 110 0 1 011 1 0 01100 0 0 101 0 0 110 0 0 111 0 1 0andtruth tableEECS150 - Fall 2001 1-18A' B' D + A' C + B' C DB C' D' + A C' + A B D'LT =EQ =GT =K-map for EQK-map for LT K-map for GTExample: two-bit comparator00100000DA11110100BC10010000DA00001001BC01001111DA00000010BC= (A xnor C) • (B xnor D)LT and GT are similar (flip A/C and B/D)A'B'C'D' + A'BC'D + ABCD + AB'CD’EECS150 - Fall 2001 1-19two alternativeimplementations of EQwith and without XORXNOR is implemented with at least 3 simple gatesABCDEQEQExample: two-bit comparatorEECS150 - Fall 2001 1-20block diagramandtruth table4-variable K-mapfor each of the 4output functionsA2 A1 B2 B1 P8 P4 P2 P10000 0 0 0 001 000010000011 000001 00 0 0 0 001 000110 0 0 1 011 0 0 1 11 000 0 0 0 001 0 0 1 010 0 1 0 01101101100 0 0 0 001 0 0 1 110011011 1 001Example: 2x2-bit multiplierP1P2P4P8A1A2B1B2EECS150 - Fall 2001 1-21K-map for P8K-map for P4K-map for P2 K-map for P1Example: 2x2-bit multiplier00000000B1A200000111A1B200010010B1A201001000A1B200000011B1A201010110A1B200000000B1A200001000A1B2P8 = A2A1B2B1P1 = A1B1P2 = A2'A1B2+ A1B2B1'+ A2B2'B1+ A2A1'B1P4 = A2B2B1'+ A2A1'B2EECS150 - Fall 2001 1-22I8 I4 I2 I1 O8 O4 O2 O1000000010001 001 0001 0001 10011010001 0001 0101 01 01 1


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Berkeley COMPSCI 150 - Section 5 Simplification and State Minimization

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