Dual-purpose custom instruction identification algorithm based on Particle Swarm Optimization

Kamal, Mehdi; Amiri, Neda Kazemian; Kamran, Arezoo; Hoseini, Seyyed Alireza; Dehyadegari, Masoud; Noori, Hamid

doi:10.1109/asap.2010.5541012

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…It can still produce solutions when optimal algorithms fail to produce solutions for large-sized problems. Other metaheuristic algorithms like particle swarm optimization (PSO) [22], ant colony optimization (ACO) [23], [24] and simulated annealing algorithms (SA) [25] have been also introduced or adapted to address the subgraph selection problem or similar problems. However, the comparisons between these popular meta-heuristic algorithms are still missing in the existing literature.…”

Section: Related Workmentioning

confidence: 99%

A comparison of heuristic algorithms for custom instruction selection

Wang

Xiao

Liu

et al. 2016

Microprocessors and Microsystems

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Extensible processors with custom function units (CFU) that implement parts of the application code can make good trade-off between performance and flexibility. In general, deciding profitable parts of the application source code that run on CFU involves two crucial steps: subgraph enumeration and subgraph selection. In this paper, we focus on the subgraph selection problem, which has been widely recognized as a computationally difficult problem. We have formally proved that the upper bound of the number of feasible solutions for the subgraph selection problem is 3 n/3 , where n is the number of subgraph candidates. We have adapted and compared five popular heuristic algorithms: simulated annealing (SA), tabu search (TS), genetic algorithm (GA), particle swarm optimization (PSO) and ant colony optimization (ACO), for the subgraph selection problem with the objective of minimising execution time under non-overlapping constraint and acyclicity constraint. The results show that the standard SA algorithm can produce the best results while taking the least amount of time among the five standard heuristics. In addition, we have introduced an adaptive local optimum searching strategy in ACO and PSO to further improve the quality of results.

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Section: Related Workmentioning

confidence: 99%

A comparison of heuristic algorithms for custom instruction selection

Wang

Xiao

Liu

et al. 2016

Microprocessors and Microsystems

View full text Add to dashboard Cite

show abstract

“…In these methods, only the convexities of the subgraphs were important while the I/O constraint was not defined. The technique proposed in [18] was able to identify CIs with or without the I/O constraint. In all of the proposed identification methods, increasing the cycle saving of the selected CIs has been the goal when only observing the I/O constraint and convexity of the selected CI.…”

Section: B Process Variation Impact On Isa Extensionmentioning

confidence: 99%

Considering the effect of process variations during the ISA extension design flow

Kamal

Afzali-Kusha

Safari

et al. 2013

Microprocessors and Microsystems

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Abstract-In this paper, we present a technique for custom instruction (CI) extension considering process variations. Thetechnique bridges the gap between the high level custom instruction extension and chip fabrication in nanotechnologies. In particular, instead of using the conventional static timing analysis (STA), it utilizes statistical static timing analysis (SSTA). Therefore, the approach becomes probabilistic where the delay of each CI is modeled by a Probability Density Function (PDF).Using this probabilistic approach, different subsets of the CIs extension are identified to meet predefined constraints (identification phase) and eventually selected for realization to improve a given merit function (selection phase). In the identification phase, performance yield under both random and systematic variations is added as a constraint. Also, a pruning technique is proposed to decrease the runtime of the systematic variation modeling. The results show that the technique reduces the number of the CIs which need systematic variation modeling by about 24.6% for the cases studied in this work. In the selection phase, both greedy and branch-and-bound approaches are used. In the greedy approach, the conventional merit function based on the cycle saving and area is modified to include the performance yield. The results show the proposed merit function leads to about 3.2% increasing in the speedup. In the branch-and-bound method an effective pruning technique is described to reduce the runtime. The pruning technique is able to reduce the search space about 62%.

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Dual-purpose custom instruction identification algorithm based on Particle Swarm Optimization

Cited by 2 publications

References 17 publications

A comparison of heuristic algorithms for custom instruction selection

A comparison of heuristic algorithms for custom instruction selection

Considering the effect of process variations during the ISA extension design flow

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