A Parallel Optimization of the Fast Algorithm of Convolution Neural Network on CPU

Huang, Jiahao; Wang, Tiejun; Zhu, Xuhui; Wei, Min; Wu, Tao; Wu, Xi; Huang, Min

doi:10.1109/icmtma.2018.00009

Cited by 6 publications

(3 citation statements)

References 2 publications

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“…Where, yjk = yk min + rand (0,1) (yk maxyk min) j= no of ways to find solution j= no of trails (5) Distributions of probability: -Find potential candidate likelihood. If used bees are searched, the info of the highest hierarchy is passed.…”

Section: Reducementioning

confidence: 99%

“…The Particle Swarm optimization techniques instead of Ant Bee Colony Optimization with map reduce for maintaining clustering quality. New techniques need to be applied to parallel computing principles in order to be able to scale with that data set sizes [5]. Some algorithms that are capable of handling large and semi-structured data, are K-Means and ISODATA.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimization of Data using Artificial Bee Colony Optimization with Map Reduce

Patil¹,

Mhatre²,

Patil³

et al. 2020

ITM Web Conf.

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We are developing an idea through this paper which would give any question the perfect and best answer. Existing system is not capable of classifying according to different patterns. This compromises with efficiency of the system and quality of final result. In this project Parallel Clustering optimization method is formed by amalgamation of Map-Reduce with Ant Bee Colony Optimization Technique for improving the efficiency and success of the data science method. In addition, related running services on a Hadoop network are predicted with the help of Map Reduce algorithm.

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Section: Reducementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimization of Data using Artificial Bee Colony Optimization with Map Reduce

Patil¹,

Mhatre²,

Patil³

et al. 2020

ITM Web Conf.

View full text Add to dashboard Cite

show abstract

“…Deep learning algorithms based on convolutional neural networks involve many floating point operations. CNN models mostly run in the environment of CPU [16] and GPU [17,18]. Although GPU can achieve real-time processing, its expensive cost and high power consumption make it difficult to satisfy the application requirements of edge computing scenarios.…”

Section: Introductionmentioning

confidence: 99%

Efficient Distributed Mapping-Based Computation for Convolutional Neural Networks in Multi-Core Embedded Parallel Environment

Jia,

Li,

et al. 2023

Electronics

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Embedded systems are the best solution to achieve high-performance edge terminal computing tasks. With the rapid increase in the amount of data generated by edge devices, it is imperative to implement intelligent algorithms with large amounts of data and computation on embedded terminal systems. In this paper, a novel multi-core ARM-based embedded hardware platform with a three-dimensional mesh structure was first established to support the decentralized algorithms. To deploy deep convolutional neural networks (CNNs) in this embedded parallel environment, a distributed mapping mechanism was proposed to efficiently decentralize computation tasks in the form of a multi-branch assembly line. In addition, a dimensionality reduction initialization method was also utilized to successfully resolve the conflict between the storage requirement of computation tasks and the limited physical memories. LeNet-5 networks with different sizes were optimized and implemented in the embedded platform to verify the performance of our proposed strategies. The results showed that memory usage can be controlled within the usable range through dimensionality reduction. The down-sampling layer as the base point of the mapping for the inter-layer segmentation achieved the optimal operation in lateral dispersion with a reduction of around 10% in the running time compared with the other layers. Further, the computing speed for a network with an input size of 105 × 105 in the multi-core parallel environment is nearly 20 times faster than that in a single-core system. This paper provided a feasible strategy for edge deployments of artificial intelligent algorithms on multi-core embedded devices.

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A Survey of Artificial Neural Network Computing Systems

Foukalas

2024

Cogn Comput

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A Parallel Optimization of the Fast Algorithm of Convolution Neural Network on CPU

Cited by 6 publications

References 2 publications

Optimization of Data using Artificial Bee Colony Optimization with Map Reduce

Optimization of Data using Artificial Bee Colony Optimization with Map Reduce

Efficient Distributed Mapping-Based Computation for Convolutional Neural Networks in Multi-Core Embedded Parallel Environment

A Survey of Artificial Neural Network Computing Systems

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