scholarly journals Memory and I/O Optimized Rectilinear Steiner Minimum Tree Routing For VLSI

2020 ◽  
Vol 9 (1) ◽  
pp. 46-59
Author(s):  
Latha N R ◽  
G R Prasad
Keyword(s):  
Optimization Technique ◽  
Computation Time ◽  
Divide And Conquer ◽  
Wire Length ◽  
Interconnect Delay ◽  
Memory Overhead ◽  
Tree Construction ◽  
Proposed Model ◽  
Steiner Minimum Tree ◽  
Disk Access

As the size of devices are scaling down at rapid pace, the interconnect delay play a major part in performance of IC chips. Therefore minimizing delay and wire length is the most desired objective. FLUTE (Fast Look-Up table) presented a fast and accurate RSMT (Rectilinear Steiner Minimum Tree) construction for both smaller and higher degree net. In this paper, FLUTE presented an optimization technique that reduces time complexity for RSMT construction for both smaller and larger degree nets. However for larger degree net this technique induces memory overhead, as it does not consider the memory requirement in constructing RSMT. Since availability of memory is very less and is expensive, it is desired to utilize memory more efficiently which in turn results in reducing I/O time (i.e. reduce the number of I/O disk access). The proposed work presents a Memory Optimized RSMT (MORSMT) construction in order to address the memory overhead for larger degree net. The depth-first search and divide and conquer approach is adopted to build a Memory optimized tree. Experiments are conducted to evaluate the performance of proposed approach over existing model for varied benchmarks in term of computation time, memory overhead and wire length. The experimental results show that the proposed model is scalable and efficient.

scholarly journals Memory and I/O optimized rectilinear steiner minimum tree routing for VLSI

2020 ◽  
Vol 10 (3) ◽  
pp. 2959
Author(s):  
Latha N. R. ◽  
G.R. Prasad
Keyword(s):  
Optimization Technique ◽  
Computation Time ◽  
Divide And Conquer ◽  
Wire Length ◽  
Interconnect Delay ◽  
Memory Overhead ◽  
Tree Construction ◽  
Proposed Model ◽  
Steiner Minimum Tree ◽  
Disk Access

As the size of devices are scaling down at rapid pace, the interconnect delay play a major part in performance of IC chips. Therefore minimizing delay and wire length is the most desired objective. FLUTE (Fast Look-Up table) presented a fast and accurate RSMT (Rectilinear Steiner Minimum Tree) construction for both smaller and higher degree net. FLUTE presented an optimization technique that reduces time complexity for RSMT construction for both smaller and larger degree nets. However for larger degree net this technique induces memory overhead, as it does not consider the memory requirement in constructing RSMT. Since availability of memory is very less and is expensive, it is desired to utilize memory more efficiently which in turn results in reducing I/O time (i.e. reduce the number of I/O disk access). The proposed work presents a Memory Optimized RSMT (MORSMT) construction in order to address the memory overhead for larger degree net. The depth-first search and divide and conquer approach is adopted to build a Memory optimized tree. Experiments are conducted to evaluate the performance of proposed approach over existing model for varied benchmarks in terms of computation time, memory overhead and wire length. The experimental results show that the proposed model is scalable and efficient.

Towards Intelligent Road Traffic Management over Weighted Large Graphs Hybrid Meta-heuristic-Based Approach

2022 ◽  
Vol 24 (3) ◽  
pp. 0-0
Keyword(s):  
Traffic Management ◽  
Large Scale ◽  
Road Traffic ◽  
Optimization Technique ◽  
Shortest Paths ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Computation Time ◽  
Exact Algorithm ◽  
On The Road

This paper introduces a new approach of hybrid meta-heuristics based optimization technique for decreasing the computation time of the shortest paths algorithm. The problem of finding the shortest paths is a combinatorial optimization problem which has been well studied from various fields. The number of vehicles on the road has increased incredibly. Therefore, traffic management has become a major problem. We study the traffic network in large scale routing problems as a field of application. The meta-heuristic we propose introduces new hybrid genetic algorithm named IOGA. The problem consists of finding the k optimal paths that minimizes a metric such as distance, time, etc. Testing was performed using an exact algorithm and meta-heuristic algorithm on random generated network instances. Experimental analyses demonstrate the efficiency of our proposed approach in terms of runtime and quality of the result. Empirical results obtained show that the proposed algorithm outperforms some of the existing technique in term of the optimal solution in every generation.

A Practical Approach to Enhancement of Accuracy of Similarity Model Using WordNet towards Semantic Service Discovery

2014 ◽  
pp. 245-265
Author(s):  
Chellammal Surianarayanan ◽  
Gopinath Ganapathy ◽  
Manikandan Sethunarayanan Ramasamy
Keyword(s):  
Web Service ◽  
Service Discovery ◽  
Service Providers ◽  
Computation Time ◽  
Proposed Model ◽  
Similarity Model ◽  
Domain Ontologies ◽  
Semantic Service ◽  
General Ontology ◽  
Semantic Web Service Discovery

Semantic Web service discovery provides high retrieval accuracy. However, it imposes an implicit constraint to service clients that the clients must express their queries with the same domain ontologies as used by the service providers. Fulfilling this criterion is very tedious. Hence, a WordNet (general ontology)-based similarity model is proposed for service discovery, and its accuracy is enhanced to a level comparable to the accuracy of computing similarity using service specific ontologies. This is done by optimizing similarity threshold, which refers to a minimum similarity that is required to decide whether a given pair of services is similar or not. The proposed model is implemented and results are presented. The approach warrants clients to express their queries without specifying any ontology and alleviates the problem of maintaining complex domain ontologies. Moreover, the computation time of WordNet-based model is very low when compared to specific ontology-based model.

Performance Evaluation of Population Seeding Techniques of Permutation-Coded GA Traveling Salesman Problems Based Assessment

2021 ◽  
pp. 1074-1115
Author(s):  
Victer Paul ◽  
Ganeshkumar C ◽  
Jayakumar L
Keyword(s):  
Genetic Algorithms ◽  
Nearest Neighbor ◽  
Optimization Technique ◽  
Optimal Solution ◽  
Computation Time ◽  
Search Space ◽  
Population Based ◽  
Vital Role ◽  
Traveling Salesman ◽  
Performance Criteria

Genetic algorithms (GAs) are a population-based meta-heuristic global optimization technique for dealing with complex problems with a very large search space. The population initialization is a crucial task in GAs because it plays a vital role in the convergence speed, problem search space exploration, and also the quality of the final optimal solution. Though the importance of deciding problem-specific population initialization in GA is widely recognized, it is hardly addressed in the literature. In this article, different population seeding techniques for permutation-coded genetic algorithms such as random, nearest neighbor (NN), gene bank (GB), sorted population (SP), and selective initialization (SI), along with three newly proposed ordered-distance-vector-based initialization techniques have been extensively studied. The ability of each population seeding technique has been examined in terms of a set of performance criteria, such as computation time, convergence rate, error rate, average convergence, convergence diversity, nearest-neighbor ratio, average distinct solutions and distribution of individuals. One of the famous combinatorial hard problems of the traveling salesman problem (TSP) is being chosen as the testbed and the experiments are performed on large-sized benchmark TSP instances obtained from standard TSPLIB. The scope of the experiments in this article is limited to the initialization phase of the GA and this restricted scope helps to assess the performance of the population seeding techniques in their intended phase alone. The experimentation analyses are carried out using statistical tools to claim the unique performance characteristic of each population seeding techniques and best performing techniques are identified based on the assessment criteria defined and the nature of the application.

scholarly journals Variable Parameters for a Single Exponential Model of Photovoltaic Modules in Crystalline-Silicon

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2138 ◽  
Author(s):  
Ali Murtaza ◽  
Umer Munir ◽  
Marcello Chiaberge ◽  
Paolo Di Leo ◽  
Filippo Spertino
Keyword(s):  
Crystalline Silicon ◽  
Weather Condition ◽  
Computation Time ◽  
Weather Conditions ◽  
Iterative Solution ◽  
Exponential Model ◽  
Standard Test ◽  
Photovoltaic Module ◽  
Proposed Model ◽  
Power Point

The correct approximation of parallel resistance (Rp) and series resistance (Rs) poses a major challenge for the single diode model of the photovoltaic module (PV). The bottleneck behind the limited accuracy of the model is the static estimation of resistive parameters. This means that Rp and Rs, once estimated, usually remain constant for the entire operating range of the same weather condition, as well as for other conditions. Another contributing factor is the availability of only standard test condition (STC) data in the manufacturer’s datasheet. This paper proposes a single-diode model with dynamic relations of Rp and Rs. The relations not only vary the resistive parameters for constant/distinct weather conditions but also provide a non-iterative solution. Initially, appropriate software is used to extract the data of current-voltage (I-V) curves from the manufacturer’s datasheet. By using these raw data and simple statistical concepts, the relations for Rp and Rs are designed. Finally, it is proved through root mean square error (RMSE) analysis that the proposed model holds a one-tenth advantage over numerous recently proposed models. Simultaneously, it is low complex, iteration-free (0 to voltage in maximum power point Vmpp range), and requires less computation time to trace the I-V curve.

scholarly journals A Single Error Correcting Code with One-Step Group Partitioned Decoding Based on Shared Majority-Vote

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 709
Author(s):  
Abhishek Das ◽  
Nur A. Touba
Keyword(s):  
Optimization Technique ◽  
Error Correcting Code ◽  
Latin Square ◽  
Error Correcting Codes ◽  
Trade Off ◽  
Cache Memories ◽  
Hamming Codes ◽  
Single Error ◽  
Memory Overhead ◽  
On Chip

Technology scaling has led to an increase in density and capacity of on-chip caches. This has enabled higher throughput by enabling more low latency memory transfers. With the reduction in size of SRAMs and development of emerging technologies, e.g., STT-MRAM, for on-chip cache memories, reliability of such memories becomes a major concern. Traditional error correcting codes, e.g., Hamming codes and orthogonal Latin square codes, either suffer from high decoding latency, which leads to lower overall throughput, or high memory overhead. In this paper, a new single error correcting code based on a shared majority voting logic is presented. The proposed codes trade off decoding latency in order to improve the memory overhead posed by orthogonal Latin square codes. A latency optimization technique is also proposed which lowers the decoding latency by incurring a slight memory overhead. It is shown that the proposed codes achieve better redundancy compared to orthogonal Latin square codes. The proposed codes are also shown to achieve lower decoding latency compared to Hamming codes. Thus, the proposed codes achieve a balanced trade-off between memory overhead and decoding latency, which makes them highly suitable for on-chip cache memories which have stringent throughput and memory overhead constraints.

scholarly journals Modeling and Simulation of Collision-Causing Derailment to Design the Derailment Containment Provision Using a Simplified Vehicle Model

2019 ◽  
Vol 10 (1) ◽  
pp. 118 ◽  
Author(s):  
In-Ho Song ◽  
Jun-Woo Kim ◽  
Jeong-Seo Koo ◽  
Nam-Hyoung Lim
Keyword(s):  
Computation Time ◽  
Test Results ◽  
Vehicle Model ◽  
Surrounding Structure ◽  
Proposed Model ◽  
Design Loads ◽  
Simulation Results ◽  
Real Test ◽  
Great Damage ◽  
Barrier Wall

As the operating speed of a train increases, there is a growing interest in reducing damage caused by derailment and collision accidents. Since a collision with the surrounding structure after a derailment accident causes a great damage, protective facilities like a barrier wall or derailment containment provision (DCP) are installed to reduce the damage due to the secondary collision accident. However, the criteria to design a protective facility such as locations and design loads are not clear because of difficulties in predicting post-derailment behaviors. In this paper, we derived a simplified frame model that can predict post derailment behaviors in the design phase of the protective facilities. The proposed vehicle model can simplify for various frames to reduce the computation time. Also, the actual derailment tests were conducted on a real test track to verify the reliability of the model. The simulation results of the proposed model showed reasonable agreement to the test results.

scholarly journals Optimization technique combined with deep learning method for teeth recognition in dental panoramic radiographs

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Fahad Parvez Mahdi ◽  
Kota Motoki ◽  
Syoji Kobashi
Keyword(s):  
Dental Care ◽  
Cross Validation ◽  
Optimization Technique ◽  
Confidence Score ◽  
Computer Assisted ◽  
Proposed Model ◽  
Tenfold Cross Validation ◽  
Stress Fatigue ◽  
Assisted Analysis ◽  
High Degree

Abstract Computer-assisted analysis of dental radiograph in dentistry is getting increasing attention from the researchers in recent years. This is mainly because it can successfully reduce human-made error due to stress, fatigue or lack of experience. Furthermore, it reduces diagnosis time and thus, improves overall efficiency and accuracy of dental care system. An automatic teeth recognition model is proposed here using residual network-based faster R-CNN technique. The detection result obtained from faster R-CNN is further refined by using a candidate optimization technique that evaluates both positional relationship and confidence score of the candidates. It achieves 0.974 and 0.981 mAPs for ResNet-50 and ResNet-101, respectively with faster R-CNN technique. The optimization technique further improves the results i.e. F1 score improves from 0.978 to 0.982 for ResNet-101. These results verify the proposed method’s ability to recognize teeth with high degree of accuracy. To test the feasibility and robustness of the model, a tenfold cross validation (CV) is presented in this paper. The result of tenfold CV effectively verifies the robustness of the model as the average F1 score obtained is more than 0.970. Thus, the proposed model can be used as a useful and reliable tool to assist dental care professionals in dentistry.

scholarly journals A New Click-Through Rates Prediction Model Based on Deep&Cross Network

Algorithms ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 342
Author(s):  
Guojing Huang ◽  
Qingliang Chen ◽  
Congjian Deng
Keyword(s):  
Neural Networks ◽  
Prediction Model ◽  
Deep Neural Networks ◽  
Prediction Models ◽  
State Of The Art ◽  
Online Advertising ◽  
Optimization Technique ◽  
Proposed Model ◽  
Great Progress ◽  
Online Advertisement

With the development of E-commerce, online advertising began to thrive and has gradually developed into a new mode of business, of which Click-Through Rates (CTR) prediction is the essential driving technology. Given a user, commodities and scenarios, the CTR model can predict the user’s click probability of an online advertisement. Recently, great progress has been made with the introduction of Deep Neural Networks (DNN) into CTR. In order to further advance the DNN-based CTR prediction models, this paper introduces a new model of FO-FTRL-DCN, based on the prestigious model of Deep&Cross Network (DCN) augmented with the latest optimization technique of Follow The Regularized Leader (FTRL) for DNN. The extensive comparative experiments on the iPinYou datasets show that the proposed model has outperformed other state-of-the-art baselines, with better generalization across different datasets in the benchmark.

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