Parametric reconfigurable designs with Machine Learning Optimizer

Kurek, Maciej; Luk, Wayne

doi:10.1109/fpt.2012.6412120

Cited by 6 publications

(3 citation statements)

References 8 publications

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“…Similarly to previous works, in [40], PSO is again improved with a learning model for parameter control, obtaining competitive performance compared to other parameter adaptation strategies. In [41], the hybridization of PSO, Gaussian process regression, and support vector machine is presented for real-time parameter tuning. The study concluded that hybridization offers superior performance compared to traditional approaches.…”

Section: Related Workmentioning

confidence: 99%

Escaping Stagnation through Improved Orca Predator Algorithm with Deep Reinforcement Learning for Feature Selection

Olivares,

Ravelo,

Soto

et al. 2024

Preprint

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Stagnation at local optima represents a significant challenge in bio–inspired optimization algorithms, often leading to suboptimal solutions. This paper addresses this issue by proposing a hybrid model that combines the Orca Predator Algorithm with Deep Q–Learning. Orca Predator Algorithm is an optimization technique that mimics the hunting behavior of orcas. It solves complex optimization problems by exploring and exploiting search spaces efficiently. Deep Q–Learning is a reinforcement learning technique that combines Q–Learning with deep neural networks. This integration aims to turn the stagnation problem into an opportunity for more focused and effective exploitation, enhancing the optimization technique’s performance and accuracy. The proposed hybrid model leverages the biomimetic strengths of Orca Predator Algorithm to identify promising regions nearby in the search space, complemented by the fine–tuning capabilities of Deep Q–Learning to navigate these areas precisely. The practical application of this approach is evaluated using the high–dimensional Heartbeat Categorization Dataset, focusing on the feature selection problem. This dataset, comprising complex electrocardiogram signals, provided a robust platform for testing the feature selection capabilities of our hybrid model. Our experimental results are encouraging, showcasing the hybrid strategy capability to identify relevant features without significantly compromising the performance metrics of machine learning models. This analysis was performed by comparing the improved method of Orca Predator Algorithm against its native version and a set of state–of–the–art algorithms.

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Section: Related Workmentioning

confidence: 99%

Escaping Stagnation through Improved Orca Predator Algorithm with Deep Reinforcement Learning for Feature Selection

Olivares,

Ravelo,

Soto

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…[39], PSO is again enhanced with a learning model to control its parameters, obtaining a competitive performance compared to other parameter adaptation strategies. The manuscript [40] presents the hybridization between PSO, Gaussian Process Regression, and Support Vector Machine for real-time parameter adjustment. The study concluded that the hybrid offers superior performance compared to traditional approaches.…”

Section: Related Workmentioning

confidence: 99%

A Learning—Based Particle Swarm Optimizer for Solving Mathematical Combinatorial Problems

Olivares,

Soto,

Crawford

et al. 2023

Axioms

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This paper presents a set of adaptive parameter control methods through reinforcement learning for the particle swarm algorithm. The aim is to adjust the algorithm’s parameters during the run, to provide the metaheuristics with the ability to learn and adapt dynamically to the problem and its context. The proposal integrates Q–Learning into the optimization algorithm for parameter control. The applied strategies include a shared Q–table, separate tables per parameter, and flexible state representation. The study was evaluated through various instances of the multidimensional knapsack problem belonging to the NP-hard class. It can be formulated as a mathematical combinatorial problem involving a set of items with multiple attributes or dimensions, aiming to maximize the total value or utility while respecting constraints on the total capacity or available resources. Experimental and statistical tests were carried out to compare the results obtained by each of these hybridizations, concluding that they can significantly improve the quality of the solutions found compared to the native version of the algorithm.

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“…In [12] the authors investigate the use of advanced meta-heuristic techniques along with machine learning to automate the optimization of a reconfigurable application parameter set. The approach proposed in [12] is called the Machine Learning Optimizer (MLO), and involves a Particle Swarm Optimization (PSO) methodology along with an underlying surrogate fitness function model based on Support Vector Machines (SVM) and a Gaussian Process (GP). Their approach is mainly used to save time on analysis and application specific tool development.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Framework for Floor-plan Prediction of Dynamic Runtime Reconfigurable Systems

Al-Wattar

Areibi

Gréwal

2015

IJRES

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Several embedded application domains for reconfigurable systems tend to combine frequent changes with high performance demands of their workloads such as image processing, wearable computing and network processors. Time multiplexing of reconfigurable hardware resources raises a number of new issues, ranging from run-time systems to complex programming models that usually form a Reconfigurable hardware Operating System (ROS). The Operating System performs online task scheduling and handles resource management. There are many challenges in adaptive computing and dynamic reconfigurable systems. One of the major understudied challenges is estimating the required resources in terms of soft cores, Programmable Reconfigurable Regions (PRRs), the appropriate communication infrastructure, and to predict a near optimal layout and floorplan of the reconfigurable logic fabric. Some of these issues are specific to the application being designed, while others are more general and relate to the underlying run-time environment. Static resource allocation for Run-Time Reconfiguration (RTR) often leads to inferior and unacceptable results. In this paper, we present a novel adaptive and dynamic methodology, based on a Machine Learning approach, for predicting and estimating the necessary resources for an application based on past historical information. An important feature of the proposed methodology is that the system is able to learn and generalize and, therefore, is expected to improve its accuracy over time. The goal of the entire process is to extract useful hidden knowledge from the data. This knowledge is the prediction and estimation of the necessary resources for an unknown or not previously seen application.

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Parametric reconfigurable designs with Machine Learning Optimizer

Cited by 6 publications

References 8 publications

Escaping Stagnation through Improved Orca Predator Algorithm with Deep Reinforcement Learning for Feature Selection

Escaping Stagnation through Improved Orca Predator Algorithm with Deep Reinforcement Learning for Feature Selection

A Learning—Based Particle Swarm Optimizer for Solving Mathematical Combinatorial Problems

An Efficient Framework for Floor-plan Prediction of Dynamic Runtime Reconfigurable Systems

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