Automated Synthesis of Data Paths in Digital Systems

Tseng, Chia-Jeng; Siewiorek, Dan

doi:10.1109/tcad.1986.1270207

Cited by 429 publications

(3 citation statements)

References 13 publications

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“…In the literature, a problem that was studied intensively is to compute a maximal clique-partition with the smallest number of cliques. Tseng's algorithm [17], introduced to solve this problem, was motivated by its application in the design of processors. Later, Bhasker and Samad [3] proposed two other algorithms.…”

Section: Preliminariesmentioning

confidence: 99%

Enumerating All Maximal Clique-Partitions of an Undirected Graph

Marin,

Kutsia,

Pau

et al. 2023

Electron. Proc. Theor. Comput. Sci.

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We address the problem of enumerating all maximal clique-partitions of an undirected graph and present an algorithm based on the observation that every maximal clique-partition can be produced from the maximal clique-cover of the graph by assigning the vertices shared among maximal cliques, to belong to only one clique. This simple algorithm has the following drawbacks: (1) the search space is very large; (2) it finds some clique-partitions which are not maximal; and (3) some cliquepartitions are found more than once. We propose two criteria to avoid these drawbacks. The outcome is an algorithm that explores a much smaller search space and guarantees that every maximal cliquepartition is computed only once.The algorithm can be used in problems such as anti-unification with proximity relations or in resource allocation tasks when one looks for several alternative ways to allocate resources.Enumerating All Maximal Clique-Partitions has to be dealt with in a special way. Proximity relations (reflexive symmetric fuzzy binary relations) characterize the notion of 'being close' numerically. They become crisp once we fix the threshold from which on, the distance between the objects can be called 'close'. Symbolic constraint solving (for unification, matching, and anti-unification constraints) over proximity relations has been studied recently by various authors, e.g., [11,12,1,7,8]. The approaches can be characterized as class-based and block-based. Considering proximity relations as (weighted) undirected graphs, a proximity class of a vertex is its neighborhood (i.e., the set of vertices to which the current vertex is connected by an edge), while a proximity block is a clique. In the class-based approach to proximity constraint solving, two objects are considered proximal if one of them belongs to the proximity class of another. In the block-based approach, two objects are proximal if they belong to the same unique maximal proximity block. The block-based approach is one that is closely related to the subject of this paper. To compute a minimal complete set of generalizations of two first-order logic terms with this approach, one needs to consider all maximal clique-partitions of the graph induced by the proximity relation between constants and between function symbols. For instance, if a is close to both b and c, but b and c are not close to each other, then f (a, a) and f (b, c) have two minimal common generalizations: f (a, x) and f (x, a). In this example, the proximity graph would be ({a, b, c}, {(a, b), (a, c)}). It has two maximal clique partitions {{a, b}, {c}} and {{a, c}, {b}} that tell exactly which symbols should be considered the same. In the first case these are a and b, leading to the generalization f (a, x), and in the second case they are a and c, giving f (x, a). Also, in the block-based approach to approximate unification, one would need to maintain maximal clique-partitions of the proximity graph in order to detect that, e.g., f (x, x) and f (b, c) are not unifiable in the abovementioned proximity r...

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Section: Preliminariesmentioning

confidence: 99%

Enumerating All Maximal Clique-Partitions of an Undirected Graph

Marin,

Kutsia,

Pau

et al. 2023

Electron. Proc. Theor. Comput. Sci.

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“…Hence, many research achievements are difficult to translate into practical engineering solutions. In this paper, we consider practical constraints of the sensor, including the ground resolution, time window, observation duration, maximum observation time, slewing angle, and pitching angle, and propose an improved task clustering based on the approximate clique partition algorithm proposed by Tseng [7].…”

Section: Introductionmentioning

confidence: 99%

A GA-SA Hybrid Planning Algorithm Combined with Improved Clustering for LEO Observation Satellite Missions

Long

et al. 2019

Algorithms

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This paper presents a space mission planning tool, which was developed for LEO (Low Earth Orbit) observation satellites. The tool is focused on a two-phase planning strategy with clustering preprocessing and mission planning, where an improved clustering algorithm is applied, and a hybrid algorithm that combines the genetic algorithm with the simulated annealing algorithm (GA–SA) is given and discussed. Experimental simulation studies demonstrate that the GA–SA algorithm with the improved clique partition algorithm based on the graph theory model exhibits higher fitness value and better optimization performance and reliability than the GA or SA algorithms alone.

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“…A clique is a fully connected subgraph of the original graph G. The signicance of a clique is that all partitions in the clique can be placed in the same physical memory. An exact solution of the clique partitioning is known to be NP-complete, so we employ an existing approximation algorithm [9] for this purpose. Each subgraph resulting from the clique partitioning corresponds to a logical memory.…”

Section: Arrays Logical Array Partitions Logical Memoriesmentioning

confidence: 99%

Low power mapping of behavioral arrays to multiple memories

Panda¹,

Dutt²

Proceedings of 1996 International Symposium on Low Power Electronics and Design

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Large data arrays in behavioral specications are usually mapped to o-chip memories during system synthesis. We address the problem of system power reduction through transition count minimization on the address bus during memory accesses, when mapping behavioral arrays to multiple memory modules drawn from a library. We formulate the problem as three l o gical-tophysical memory mapping subtasks, provide algorithms for each subtask, and present experiments that demonstrate the transition count reductions based on our approach. Our experiments show a transition count reduction by a factor of 1.5{6.7 over a straightforward mapping scheme.

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Automated Synthesis of Data Paths in Digital Systems

Abstract: This paper presents a unifying procedure, called Facet, for the automated synthesis of data paths at the register-transfer level.

Cited by 429 publications

References 13 publications

Enumerating All Maximal Clique-Partitions of an Undirected Graph

Enumerating All Maximal Clique-Partitions of an Undirected Graph

A GA-SA Hybrid Planning Algorithm Combined with Improved Clustering for LEO Observation Satellite Missions

Low power mapping of behavioral arrays to multiple memories

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