Switching theory is the theory of circuits made up of ideal digital devices, including their structure, behavior, and design. It incorporates Boolean logic, a basic component of modern digital switching systems. This book provides an in-depth knowledge of switching theory and the design techniques of digital circuits and presents a brief discussion of the basics of logic design. Switching circuit theory is the analytical investigation of the attributes of systems.
Sequential organizational behavior is utilized to layout, and consequently develop, finite state machines. Boolean logic is broken down into combinational logic, together with sequential logic. In electronic components, a logic gate is an idealized, or, alternatively, an actual apparatus, employing Boolean functionality. It carries out a rational procedure upon inputs. Sequential logic is a type of Boolean logic, in which the performance is a product of both the present inputs together with past information.
Combinational logic circuits, on the other hand, rely only on current input. These functions are unable to rely on overall parameters, or other supplementary factors, such as memory.
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This will lead to spectral interpretation and a uniform treatment of decision diagrams and will be discussed below. For clarity the nodes are labelled by 1,. Conversely, this BDT represents f in the complete disjunctive normal form. Notice that both outgoing edges of the node 7 points to the value 1.
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Thus, we do not make any decision in this node. Thus, it can be deleted. Subtrees rooted in the nodes 5 and 6 are isomorphic. Thus, we share the isomorphic subtrees. Each product term corresponds to a path from the root node to the constant node with the value 1. The reduction of the number of nodes in a BDT described in the above example can be formalized as the Binary decision diagrams reduction rules BDD-reduction rules shown in Fig. Decision Diagrams over Groups It is often useful to consider decision diagrams that have a more general structure, e. Also, the values of the constant nodes can be arbitrary instead of binary or logic values 0 and 1.
An ordered decision diagram over G is a rooted directed graph with two types of nodes. Again, the function f is represented by the decision diagram in a recursive manner as follows. The value of f at v, fv x1 ,. Remark 4. We will illustrate this approach by the following example. The construction of the BDD for h is again done recursively. Let x be one of the variables. Multi-terminal binary decision diagrams MTBDDs  have been introduced to represent integer valued functions on C2n.
Table 4. Representation of f in Example 4.
Consider the functional decision tree for f x1,x2,x3 shown in Fig. Since the matrix of the Reed-Muller transform is self-inverse over GF 2 , the same relation holds also in the opposite direction, i. It is obvious that instead the pD-expansion, the negative-Davio rules can be also used to assign a given function f to a decision tree. Depending on the properties of the function represented, a Functional decision tree reduces to a Functional decision diagram in the same way as a BDT reduces to a BDD. These rules utilize the fact that when the outgoing edge of a pD-node points to the value 0, this node can be deleted since the 0-value does not contribute to the PPRM-expression.
Positive-polarity FDD for f in Example 4. Negative-polarity FDD for f in Example 4. Kronecker decision diagrams The main idea in Functional decision diagrams is to exploit fact that for some functions the Reed-Muller spectrum gives more compact decision diagrams in the number of non-terminal nodes than the original function itself. This decision diagram can be viewed as graphic representation of a functional expression in terms of basis functions.
On the other hand, the function is represented by the decision tree in Fig. Actually, this corresponds to the operations of so-called fast transforms. Notice that while compact representations are obtained, all regularity is lost. Spectral Interpretation of Decision Diagrams The examples above and their interpretation in terms of basis functions yield to the spectral interpretation of decision diagrams . This interpretation is explained in Fig. If a given function f is assigned to a decision tree by the Shannon expansion, which can be interpreted as the identical mapping, we get BDDs or MTBDDs depending on the range of functions represented.
Therefore, from a STDT, if we follow labels at the edges by starting from constant nodes, we read f by calculation the inverse expansion of the function. However, if we formally replace labels at the edges by these used in Shannon nodes, we can read the spectrum of f from a STDT. Since constant nodes in these decision diagrams show function values for f , we can compute the spectrum Sf by performing at each node calculations determined by the basic transform matrix in a Kronecker product representable spectral transform. In BDDs and MTBDDs, this means replacement of labels at the edges by the ones that are used in the corresponding spectral transform decomposition rules and then reading the spectrum by traversing paths in the diagram starting from constant nodes.
Also, the operations are done over the rationals, e. Such decision diagrams are called the word-level decision diagrams, unlike bit-level decision diagrams where constant nodes have logic values 0 and 1.
Pesudo-Kronecker decision tree and its reduction into the corresponding diagram for the function f in Example 4. Therefore, Arithmetic spectral transform decision diagrams represent functions in the form of arithmetic polynomials. Attention should be paid to the labels at the edges in this WDD, and for instance to the label at the left outgoing edge of the root node. It is determined as the sum of the outgoing edges of the left node at the level for y0 multiplied by the label at the incoming edge to this node.http://autodiscover.manualcoursemarket.com/93.php
Switching Theory And Logic Design: GATE (ECE)
This node can be deleted since both outgoing edges point to the same value 4, however, its impact has to be taken into account by changing the label at the incoming edge as described above. WDD for f in Example 4. Notice that the calculations at the levels above the last level of non-terminal nodes are performed over subfunctions represented by the subtrees rooted in the processed nodes.
If each step of the calculation is represented by a decision diagram, we get the Walsh decision diagram for f shown in Fig. Decision Diagrams for Representation of Switching Functions There is a direct correspondence between these characteristic and the basic characteristics of logic networks that are derived from decision diagrams as it will be discussed further in this book. For instance, the number of non-terminal nodes corresponds to the number of elementary modules in the corresponding networks. When calculations are performed over decision diagrams, some calculation subprocedure should be performed at each non-terminal node.
Therefore, reduction of nonterminal nodes is a chief goal in optimizing decision diagrams. The delay in a network is proportional to the depth of the decision diagram, which together with the width, determine the area occupied by the network. Edges in the diagram determine interconnections in the network.
Thus, for applications where particular parameters in the networks are important, reduction of the corresponding characteristics of decision diagrams is useful. Recall that the reduction in a BDT or MTBDT is possible if there are constant subvectors, the representation of which reduces to a single constat node, or identical subvectors, that can be represented by a shared subtree. In the case of Spectral transform decision diagrams, search for constant or identical subvectors is transferred to spectra instead of over the initial functions. It may happen that for an appropriately selected transform, the corresponding spectrum, that is, the vector of values of constant nodes, expresses more such useful regularity.
A widely exploited method for reduction of the size of decision di agrams is reordering of variables in the functions represented, because the size of decision diagrams in usually strongly depends on the order of variables. The determination of the best order of variables which produces the decision diagram of the minimum size is an NP-complete problem , .
There are many heuristic methods that often produce a nearly optimal diagram. An example are symmetric functions whose value does not depend on the order of variables. For these reasons various methods to reduce the sizes of decision diagrams by linear combination of variables have been considered, see for example , , , . MTBDDs for initial order of variables, permuted, and linearly transformed variables in f in Example 4.
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Exercises and Problems Exercise 4. Exercise 4. Compare and discuss these expressions. Determine the function f represented by this diagram and write the corresponding functional expression for f.
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S x3 1 Binary decision diagram. Determine the corresponding decision diagrams. Represent f by a Shared BDD. Determine the function f represented by this diagram and write the functional expression for f corresponding to this diagram. Determine the corresponding transform matrix in terms of which the values of constant nodes are calculated.