Efficient Deep Neural Network Serving: Fast and Furious

Yan, Feng; He, Yuxiong; Ruwase, Olatunji; Smirni, Evgenia

doi:10.1109/tnsm.2018.2808352

Cited by 12 publications

(4 citation statements)

References 36 publications

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“…Deep learning originates from the discussion and exploration of artificial neural network (ANN) and deep neural networks (DNN), and it is a deep machine learning model [ 22 ]. The deep learning executes a series of nonlinear transformation to study how to automatically extract the multilayer characteristics from the original data, and it has been widely applied in the field of image recognition, speech recognition, natural language processing, drug discovery, and so on [ 23 ].…”

Section: Computational Logistics and Deep Learningmentioning

confidence: 99%

“…However, the abstract models and automation machines of CTHS by computational logistics really need the problem-oriented, process-oriented, and scenario-oriented machine intelligence. The deep learning can take this responsibility for CTHS, and it is based on the ANN or DNN that has the excellent ability of self-learning, self-organization, self-adaptation, and strong nonlinear function approximation [ 23 ]. So the deep learning is just about the following aircraft of computational logistics.…”

Section: Computational Logistics and Deep Learningmentioning

confidence: 99%

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Computational Logistics for Container Terminal Handling Systems with Deep Learning

2021

Computational Intelligence and Neuroscience

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Container terminals are playing an increasingly important role in the global logistics network; however, the programming, planning, scheduling, and decision of the container terminal handling system (CTHS) all are provided with a high degree of nonlinearity, coupling, and complexity. Given that, a combination of computational logistics and deep learning, which is just about container terminal-oriented neural-physical fusion computation (CTO-NPFC), is proposed to discuss and explore the pattern recognition and regression analysis of CTHS. Because the liner berthing time (LBT) is the central index of terminal logistics service and carbon efficiency conditions and it is also the important foundation and guidance to task scheduling and resource allocation in CTHS, a deep learning model core computing architecture (DLM-CCA) for LBT prediction is presented to practice CTO-NPFC. Based on the quayside running data for the past five years at a typical container terminal in China, the deep neural networks model of the DLM-CCA is designed, implemented, executed, and evaluated with TensorFlow 2.3 and the specific feature extraction package of tsfresh. The DLM-CCA shows agile, efficient, flexible, and excellent forecasting performances for LBT with the low consuming costs on a common hardware platform. It interprets and demonstrates the feasibility and credibility of the philosophy, paradigm, architecture, and algorithm of CTO-NPFC preliminarily.

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Section: Computational Logistics and Deep Learningmentioning

confidence: 99%

Section: Computational Logistics and Deep Learningmentioning

confidence: 99%

Computational Logistics for Container Terminal Handling Systems with Deep Learning

2021

Computational Intelligence and Neuroscience

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“…With the use of Machine Learning in applications, the lack of computational resources become a problem. However, improved solutions with the implementation of parallelization algorithms gain performance, and this is where the work of [12] goes into detail. In this work, gains are discussed and presented by parallelizing and using services that modularize the layers of the Deep Neural Network (DNN).…”

Section: Related Workmentioning

confidence: 99%

A Microservice Based Architecture Topology for Machine Learning Deployment

Ribeiro

Figueredo

Araujo

et al. 2019

2019 IEEE International Smart Cities Conference (ISC2)

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Smart solutions that make use of machine learning and data analyses are on the rise. Big Data analysis is attracting more and more developers and researchers, and at least five requirements (Velocity, Volume, Value, Variety, and Veracity) show challenges in deploying such solutions. Across the globe, many Smart City initiatives are using Big Data Analytics as a tool for doing predictive analytics which can be helpful to human well being. This work presents a generic architecture named Machine Learning in Microservices Architecture (MLMA) that provides design patterns to transform a monolithic architecture of machine learning pipelines in microservices with separate roles. We present two case studies deployed to a Smart City initiative, where we discuss how each component of the architecture applied in specific applications that use predictions with machine learning. Among the benefits of this architecture, we argue prediction performance, scalability, code maintenance and reusability makes such transition a natural trend in Big Data and machine learning applications.

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“…Section 3 describes about the problem statement describing about the major issues, which are considered in these tasks. Section 4 explains regarding the explained HHFDS methodology that hybridizes a fuzzy Dijstra's algorithm with deep neural network 14,15 . Section 5 provides comparative analysis to depict with increased production of the explained algorithm.…”

Section: Introductionmentioning

confidence: 99%

HHFDS: Heterogeneous hybridized fuzzy‐based Dijkstra's multitask scheduling in WSN

Prakash¹,

Chelliah²,

Ramanujam

2019

Concurrency and Computation

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Summary Wireless Sensor Network (WSN) has to diffuse the independent wireless sensor nodes, which can track the physical or environmental status. In previous works, many issues prevailed with multitask scheduling such as interference, high energy consumption, throughput, etc. Moreover, some issues arise while working with heterogeneous WSN. This work deals with these issues in heterogeneous WSN with aid of the multitask scheduling. For this purpose, a Heterogeneous Hybridized Fuzzy‐based (depends on degree of truth) Dijkstra's organizing algorithm (HHFDS) was proposed. This technique integrates light weight characteristics as well as a queuing‐based analysis methods, hence identifying the greatest parallel arrangements for which the provided data packet effectively. In this technique, fuzzy Dijkstra's algorithm is hybridized with deep neural network. This also depicts in what way to utilize the identifications outcome with varying data packets and thereby helps with various heterogeneous organizing objectives. The proposed HHFDS algorithm is implemented via the use of the network simulator (NS2). The results of the proposed and existing methods are measured in terms of the metrics like Energy Consumption, Time Consumption, Transmission Delay, and Average Throughput. The simulation results are depicted as follows and the comparison result shown as follows demonstrates the effectiveness of the proposed algorithm.

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Efficient Deep Neural Network Serving: Fast and Furious

Cited by 12 publications

References 36 publications

Computational Logistics for Container Terminal Handling Systems with Deep Learning

Computational Logistics for Container Terminal Handling Systems with Deep Learning

A Microservice Based Architecture Topology for Machine Learning Deployment

HHFDS: Heterogeneous hybridized fuzzy‐based Dijkstra's multitask scheduling in WSN

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