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A. J. Bernstein

dblp:180/6362 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 1965
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Electronic design automation · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Electronic design automation › logic synthesis
combinational logic synthesis
0.011965
State Assignments in Combinational Networks · IEEE Trans. Electron. Comput. 1965
Electronic design automation
logic synthesis
0.011965
State Assignments in Combinational Networks · IEEE Trans. Electron. Comput. 1965
Electronic design automation › logic synthesis › finite state machine synthesis
output encoding
0.011965
State Assignments in Combinational Networks · IEEE Trans. Electron. Comput. 1965
Electronic design automation › logic synthesis
state assignment
0.011965
State Assignments in Combinational Networks · IEEE Trans. Electron. Comput. 1965
YearPublicationVenuePosition
1965 State Assignments in Combinational Networks
abstract
The problem of assigning codes to the output states of a multiple-output combinational circuit is considered. It is assumed that if the circuit has n inputs, then the 2nfundamental products, i.e., input states, are to be partitioned into disjoint groups, such that all members of the same group produce the same output state. The problem of coding the output states is studied here. Two algorithms for making the assignments are considered. The first gives those encodings for which the sum of the costs of all the output functions is minimum; the second minimizes the variable dependency of the output functions. In problems where reduced variable dependency is possible it has been found that the second algorithm yields minimum or near-minimum cost networks. Since this algorithm is easily applied it is useful for finding economical networks in situations where a large number of variables are involved since in such cases the first algorithm becomes lengthy. Attention is also directed to the problem of determining the optimum number of output variables to use for an encoding. An upper bound is derived and an example is presented which requires this bound.
A. J. Nichols, A. J. Bernstein
IEEE Trans. Electron. Comput.2