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We present here a technique for allocation and binding for data path synthesis (DPS) using a Genetic Algorithm (GA) approach. This GA uses an unconventional crossover mechanism relying on a force directed data path binding completion algorithm. The data path is synthesized using some supplied design parameters. A bus-based interconnection scheme, use of multi-port memories, and provision for multicycling and pipelining are the main features of this system. The method presented here has been applied to standard benchmark examples and the results obtained are promising. Index Terms-Allocation, binding, data path synthesis. I. INTRODUCTION Data path synthesis (DPS) involves scheduling of operations followed by allocation and binding. The latter step consists of several sub-tasks which include determining the mix of functional units (FUs) grouping variables and assigning these variable clusters to storage units, memory port assignment when multi-port memories are used in the design, mapping operations to the FUs, and mapping transfers to buses, when buses are used. The present work is concerned with the allocation and binding aspects of DPS. Earlier techniques attempted to solve these sub-tasks independently. Contemporary techniques attempt to handle the sub-tasks and other tasks such as scheduling in larger groups to ensure better optimality of the final design. However, all these sub-tasks are known to be NP-Complete and amalgamating many of these as a single problem is computationally prohibitive. It is therefore desirable to be able to solve the sub-tasks in overlapping combinations and move from one set of sub-tasks to another with a set of solutions rather than just a single solution. Multiple heuristics and randomization schemes may be used to find multiple solutions that are of the same cost or are nondominating. These concerns have motivated us to develop a Genetic Algorithm (GA), called GABIND, for synthesizing optimized data paths from a given scheduled data flow graph. GABIND builds on previously developed successful heuristics, such as force [1], by incorporating them into the GA. Several researchers have worked on the DPS problems and several systems such as HAL [1], SAST [2], Facet [3], STAR [4], SAM [5], and Vital-NS [6] have been developed to solve the problem. While all techniques attempt to optimize schedule time and cost of storage and FUs, current techniques place an emphasis on interconnect optimization. HAL, SAM, Vital-NS, and STAR are some of the systems that perform interconnect optimization, in addition to the other DPS related optimizations. HAL was the first to make use of the force-directed algorithm to perform scheduling and data path optimizations. SAM combines scheduling, allocation, and mapping in a single algorithm. The algorithm uses the notion of force [1] to measure the effect that a tentative scheduling of an operation would have on the resource requirements. VITAL-NS performs scheduling, allocation and binding sub-tasks of DPS. FU registers and buses are partially allocated Manu...

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Abstract. In this paper, we consider the problem of designing virtual topologies for multihop optical WDM networks when the traffic is self-similar in nature. Studies over the last few years suggest that the network traffic is bursty and can be much better modeled using self similar process instead of Poisson process. We examine buffer sizes of a network and observe that, even with reasonably low buffer overflow probability, the maximum buffer size requirement for self-similar traffic can be very large. Therefore, a self-similar traffic model has an impact on the queuing delay which is usually much higher than that obtained with the Poisson model. We investigate the problem of constructing the virtual topology with these two types of traffic and solve it with two algorithmic approaches: Greedy (Heuristic) algorithm and Evolutionary algorithm. While the greedy algorithm performs a leastcost search on the total delay along paths for routing traffic in a multihop fashion, the evolutionary algorithm uses genetic methods to optimize the average delay in a network. We analyze and compare our proposed algorithms with an existing algorithm via different performance parameters. Interestingly, with both the proposed algorithms the difference in the queuing delays, caused by self-similar and Poisson traffic, results in different multihop virtual topologies.

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