MO-PSE: Adaptive multi-objective particle swarm optimization based design space exploration in architectural synthesis for application specific processor design

Mishra, Vipul Kumar; Sengupta, Anirban

doi:10.1016/j.advengsoft.2013.09.001

Cited by 53 publications

(8 citation statements)

References 20 publications

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“…Meanwhile the appeal depends on the light force, double terms go into the figuring of 1 . In the major place, from the backwards four-sided law 1 ( ) = 0 / 2 , wherever the power of the firefly presence stimulated near [23]. At that point because of the retention through the air, 2 ( ) = 0 / − , w is the assimilation constant, and w€[0,∞).…”

Section: The Firefly Algorithmmentioning

confidence: 99%

Intrusion Detection System Employing Multi-level Feed Forward Neural Network along with Firefly Optimization (FMLF2N2)

Krishna¹,

Prakash²

2019

ISI

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Number of attacks as of late has immensely expanded because of the expansion in Internet exercises. This security issue has made the Intrusion Detection Systems (IDS) a noteworthy channel for data security. The IDS's are created to in the treatment of attacks in PC frameworks by making a database of the typical and unusual practices for the recognition of deviations from the ordinary amid dynamic interruptions. The issue of classification time is enormously diminished in the IDS through component choice. This paper is proposing the usage of IDS for the successful location of attacks. In view of this, the Firefly Algorithm (FA), another paired element determination calculation was proposed and executed. The FA chooses the ideal number of highlights from NSL dataset. Moreover, the FA was connected with multitargets relying upon the classification precision and the quantity of highlights in the meantime. This is a proficient framework for the discovery of attacks decrease of false alerts. The execution of the IDS in the location of attacks was improved by the proposed classification and highlight choice techniques.

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Section: The Firefly Algorithmmentioning

confidence: 99%

Intrusion Detection System Employing Multi-level Feed Forward Neural Network along with Firefly Optimization (FMLF2N2)

Krishna¹,

Prakash²

2019

ISI

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“…PSO is an advanced optimisation technique widely deployed in solving complex optimisation problems across several domains [29–34]. In this paper we have integrated PSO based DSE with HLS‐based fault security methodology by mapping its generic procedure to the requirements of the current problem.…”

Section: Background and Motivation Of Psomentioning

confidence: 99%

Particle swarm optimisation driven low cost single event transient fault secured design during architectural synthesis

Sengupta

Kachave

2017

J. eng.

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“…P T is composed of dynamic power ( P D ) and static power ( P S ). The cost function from [3] is

f false(S false) = ϕ_{1} \frac{P_{T} - P_{normalcons}}{P_{truemax}} + ϕ_{2} \frac{T_{E} - T_{cons}}{T_{max}}

where f ( s ) is the cost of solution S ; ϕ 1 , ϕ 2 are the user defined weights for power and execution time; P max and T max are the solutions in design space with maximum power and maximum execution time, respectively.…”

Section: Power Model and Cost Modelmentioning

confidence: 99%

“…For example, in [2], the SPARK tool was introduced where the loop unrolling factor (UF) being user‐directed is not able to explore the combination of UF and architecture together automatically. Moreover, in [3] Mishra and Sengupta proposed a particle swarm optimisation (PSO) DSE where the loop unrolling was not considered during exploration. In [4] evolutionary algorithm‐based DSE required manual intervention to decide UF besides considering only UFs which are multiples of iteration count.…”

Section: Introductionmentioning

confidence: 99%

Swarm‐inspired exploration of architecture and unrolling factors for nested‐loop‐based application in architectural synthesis

Mishra

Sengupta

2015

Electron. lett.

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A novel framework for automated design space exploration (DSE) of architecture and unrolling factor (UF) for perfectly nested loops in architectural synthesis (AS) is presented. The sub-contributions are that it: (a) proposes a novel model for determining execution time based on architecture and UFs for nested loop without tiresomely unrolling completely; (b) proposes a novel methodology for exploration of architecture and loop UFs for a perfectly nested loop, based on swarm intelligence (SI); and (c) maintains a trade-off between power and performance during exploration and also resolves orthogonal issues such as exploration speed and quality of result (QoR). Results of the proposed approach indicate an average improvement in QoR of >33% and reduction in runtime of >34% compared with recent approaches.Introduction: The design space exploration (DSE) for applicationspecific processors is intricate involving identification of best solution from a set of massive design alternatives. In [1], the DSE problem of only single-loop-based applications with no focus on nested-loop-based control data flow graph (CDFG) is solved. Moreover, the delay model proposed in [1] is not useful when the number of independent operations required to be performed in parallel due to unrolling exceeds the available resource units specified in a solution.Many researchers have addressed DSE in architectural synthesis. For example, in [2], the SPARK tool was introduced where the loop unrolling factor (UF) being user-directed is not able to explore the combination of UF and architecture together automatically. Moreover, in [3] Mishra and Sengupta proposed a particle swarm optimisation (PSO) DSE where the loop unrolling was not considered during exploration. In [4] evolutionary algorithm-based DSE required manual intervention to decide UF besides considering only UFs which are multiples of iteration count. Harish et al.[5] used a genetic algorithm and weighted sum PSO-based DSE which did not consider constraints in cost function and UF during DSE.

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MO-PSE: Adaptive multi-objective particle swarm optimization based design space exploration in architectural synthesis for application specific processor design

Cited by 53 publications

References 20 publications

Intrusion Detection System Employing Multi-level Feed Forward Neural Network along with Firefly Optimization (FMLF2N2)

Intrusion Detection System Employing Multi-level Feed Forward Neural Network along with Firefly Optimization (FMLF2N2)

Particle swarm optimisation driven low cost single event transient fault secured design during architectural synthesis

Swarm‐inspired exploration of architecture and unrolling factors for nested‐loop‐based application in architectural synthesis

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