FPGA implementation of population-based ant colony optimization

Scheuermann, Bernd; So, Keith; Guntsch, Michael; Middendorf, Martin; Diessel, Oliver; ElGindy, Hossam A.; Schmeck, Hartmut

doi:10.1016/j.asoc.2004.03.008

Cited by 48 publications

(6 citation statements)

References 33 publications

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“…A noteworthy exception to these directions is the specific design of an ACO algorithm to allow an effective hardware implementation on field-programmable gate arrays (FPGAs). 26 We follow the second direction. Thus, multiple ant colonies are employed in parallel.…”

Section: Articlementioning

confidence: 99%

Accelerating Molecular Docking Calculations Using Graphics Processing Units

Korb

Stützle

Exner

2011

J. Chem. Inf. Model.

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The generation of molecular conformations and the evaluation of interaction potentials are common tasks in molecular modeling applications, particularly in protein-ligand or protein-protein docking programs. In this work, we present a GPU-accelerated approach capable of speeding up these tasks considerably. For the evaluation of interaction potentials in the context of rigid protein-protein docking, the GPU-accelerated approach reached speedup factors of up to over 50 compared to an optimized CPU-based implementation. Treating the ligand and donor groups in the protein binding site as flexible, speedup factors of up to 16 can be observed in the evaluation of protein-ligand interaction potentials. Additionally, we introduce a parallel version of our protein-ligand docking algorithm PLANTS that can take advantage of this GPU-accelerated scoring function evaluation. We compared the GPU-accelerated parallel version to the same algorithm running on the CPU and also to the highly optimized sequential CPU-based version. In terms of dependence of the ligand size and the number of rotatable bonds, speedup factors of up to 10 and 7, respectively, can be observed. Finally, a fitness landscape analysis in the context of rigid protein-protein docking was performed. Using a systematic grid-based search methodology, the GPU-accelerated version outperformed the CPU-based version with speedup factors of up to 60.

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Section: Articlementioning

confidence: 99%

Accelerating Molecular Docking Calculations Using Graphics Processing Units

Korb

Stützle

Exner

2011

J. Chem. Inf. Model.

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“…After the MP movement has stopped, the outside observer processor collects the tuple .5, 6, 2, 10, 2, 3/ from the pixel at .8, 14/. It computes the center at .8 6 5 , 14 C 2 5 / according to Equation (2), the variances 2…”

Section: Examplementioning

confidence: 99%

“…This problem refers to so called creatures that move on a grid according to local rules with the goal to visit all grid points efficiently in an unknown environment. In , FPGAs have been used for implementing Ant Colony Optimization algorithms. The difference to the work presented here is that MPs are implemented as dynamic elements, that is as agents with states and data, which are passed from one processor node to the next one, whereas in the cited works, the agents are realized as more or less static data structures whose contents are updated with dedicated hardware.…”

Section: Related Workmentioning

confidence: 99%

Realizing real‐time centroid detection of multiple objects with marching pixels algorithms on programmable customizing hardware

Fey

Reichenbach

Komann

et al. 2011

Concurrency and Computation

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In this paper, we present a class of emergent algorithms called Marching Pixels and a corresponding programmable parallel chip architecture. Marching Pixels can be used for real-time image processing in smart camera chips. They are based on hardware agents, which are virtually crawling in a pixel grid image to find attributes like centroid, rotation, and size of an arbitrary number of objects given in an image. Because of the distributed and local processing scheme of Marching Pixels, reply times in milliseconds can be fulfilled. This means that time is determined where pre-known objects are located and how they are oriented to the main axes of the image. We present an example Marching Pixels algorithm and corresponding applicationspecific and programmable parallel architectures. The latter contains a specific instruction set that allows not only the execution of Marching Pixels algorithms but also of arbitrary Cellular Automata algorithms as an embedded parallel processor. The strengths and weaknesses of this architecture concerning the realization as field-programmable gate arrays and application-specific integrated circuits are discussed by means of hardware synthesis results. These results are compared with the solution achievable on a real hardware like the Atom processor. the camera in a way that work can be done in the requested time. The problem is, if image processing is serially performed using classic algorithms, it will be too slow even on fast single processors. For clarification, an example is given now. Let the image have classic VGA resolution of 640 480 pixels. This means a serial processor has to compute 307.200 pixels within the required reply time of 10 ms. Hence, only 32.6 ns remain for the computation of each pixel. To carry out a reasonable number of operations within this time range would require a clock cycle time of several GHz that one would like to avoid in embedded systems because of the high energy dissipation at such high frequencies.Our answer in order to both meet the strict real-time requirements and to be scalable with regard to increasing pixel resolutions are appropriate parallel low-level, that is hardware-oriented, algorithms, which are based mainly on local operators. These algorithms must not only work in parallel to be fast but also in a distributed way to be robust as well.The scalability in our algorithms is fulfilled by a kind of autonomous agents, which are instructed with the task to travel virtually within a pixel grid, which corresponds to the image, in order to find the centroids coordinates of objects given in this image. After the agents have found them, the corresponding coordinates are given to a robot control to enable the robot to grap the objects. Because these agents march around the pixels, they were given the name Marching Pixels (MPs). They have the goal to visit certain pixels and to gather data about the objects to which the pixels belong. The MP swarm shall further compress the gathered data in order to retrieve desired object information like size, ...

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“…However, a random number generator was not implementable in hardware at that time. Scheuermann and his colleagues took the single machine total tardiness problem (SMTTP) as a typical example, presenting a hardware implementation of population-based ant colony optimization (P-ACO) on field-programmable gate arrays (FPGAs) (Scheuermann et al, 2004(Scheuermann et al, , 2007. In that work, they described the P-ACO algorithm and presented a circuit architecture that facilitates efficient FPGA implementations.…”

Section: Introductionmentioning

confidence: 99%

Ant colony optimization-based bio-inspired hardware: survey and prospect

Duan

Yuan

Zou³

et al. 2010

Transactions of the Institute of Measurement and Control

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Bio-inspired hardware (BHW) refers to hardware that can change its architecture and behaviour dynamically and autonomously by interacting with its environment, and ant colony optimization is a meta-heuristic algorithm for the approximate solution of combinatorial optimization problems that has been inspired by the foraging behaviour of real ant colonies. In this paper, we take a broad survey on the recent progresses of ant colony optimization-based BHW, which includes ant colony optimization-based fuzzy controller, ant colony optimization-based hardware for the Travelling Salesman Problem (TSP), digital circuits, digital infinite impulse-response (IIR) filters, hardware-oriented ant colony optimization with look-up table and hardware/software partition. Some important issues of the challenges of ant colony optimization-based BHW are also presented. Online realization, robustness, generalization, disaster problems, theoretical analysis, implementation, swarm robotics, applications and hybrid approaches are eight key challenging issues for the ant colony optimization-based BHW.

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FPGA implementation of population-based ant colony optimization

Cited by 48 publications

References 33 publications

Accelerating Molecular Docking Calculations Using Graphics Processing Units

Accelerating Molecular Docking Calculations Using Graphics Processing Units

Realizing real‐time centroid detection of multiple objects with marching pixels algorithms on programmable customizing hardware

Ant colony optimization-based bio-inspired hardware: survey and prospect

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