The accuracy of concrete strength inspection has a great influence on the safety evaluation of the building. In order to increase the accuracy, Fuzzy Neural Network (FNN) was built up to evaluate concrete stmngth: It ...
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ISBN:
(纸本)0780382730
The accuracy of concrete strength inspection has a great influence on the safety evaluation of the building. In order to increase the accuracy, Fuzzy Neural Network (FNN) was built up to evaluate concrete stmngth: It takes full advantage of the characteristics of the common concrete testing methods: drill and rebound, and the abilities of FNN including automatic learning, generation and fuzzy logic inference. The experiment shows that the max relative error of the predicted results is 1.12%, which is satisfied with the requirements of the engineering. The method effieieatly maps the complex non-linear relationship between the drill values and the rebound values, and provides a efficient way for the concrete strength inspection and evaluation.
Heterogeneity is considered as a solution for supercomputers to scale to petascale. Many systems which are composed of general CPUs and special processing units such as Cells, GPGPUs and FPGAs have been implemented. I...
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Heterogeneity is considered as a solution for supercomputers to scale to petascale. Many systems which are composed of general CPUs and special processing units such as Cells, GPGPUs and FPGAs have been implemented. In these systems, CPU needs interact with special processing units to process data together, thus communications between these heterogeneous processing units become a key problem, and the communication subsytem should provide low latency and high bandwidth. In this paper, we propose HPP-Controller, which is designed for connecting two different types of CPUs (AMD and Loongson) in one node. It connects heterogeneous CPUs on top of no-coherent HyperTransport (HT) fabric and supports Global Physical Address Space. We implement a FPGA-based prototype and evaluate it via experiments. Initial results show that HPP-Controller has low latency of 0.75 us and high bandwidth close to bandwith of HT links.
Bias Temperature Instability (BTI) is one of the dominant CMOS aging mechanisms. It causes time-dependent variation, threatening circuit lifetime reliability. BTI-induced circuit errors are not detectable at the fabri...
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ISBN:
(数字)9781728174679
ISBN:
(纸本)9781728174686
Bias Temperature Instability (BTI) is one of the dominant CMOS aging mechanisms. It causes time-dependent variation, threatening circuit lifetime reliability. BTI-induced circuit errors are not detectable at the fabrication stage. On-line monitoring schemes are therefore necessary to capture the degradations during the operational time. Traditional aging monitoring techniques exhibit high implementation complexity and low stability. In this paper, we propose a BTI monitoring approach by simply tracking the start-up behavior of SRAM cells. SRAM is a widely used on-chip device in many applications. We study the impact of BTI for SRAM start-up values and age some cells in a manipulated manner. The BTI degradation is evaluated based on the number of SRAM cells starting with a certain value. This technique can be used to estimate the degradation for on-chip logic circuits without introducing additional circuitry, and thus has very low implementation complexity. We use an SRAM array with 1024 cells to estimate the degradations for multiple logic circuits, and show the average mean absolute percentage error as 8.48%. In addition, this technique is robust considering process, voltage and temperature variations.
In this paper, we study the lifetime optimization problem using a mobile sink node in a storage-constrained wireless sensor network, and propose an optimal data gathering mechanism named (TAPEMAN) which runs in three ...
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In this paper, we study the lifetime optimization problem using a mobile sink node in a storage-constrained wireless sensor network, and propose an optimal data gathering mechanism named (TAPEMAN) which runs in three steps: first a tight upper bound of the network lifetime is derived through analysis of energy consumption, then we generate a Traveling salesman problem (TSP) solution based on a 2-approximation $O(n^{2})$ algorithm. A judgment will be made about whether this solution is optimal. If the answer is yes, TAPEMAN terminates. If not, a novel data diffusion scheme is used to distribute data to neighboring nodes, in order to avoid data leaking. We prove that under some reasonable assumptions, our algorithm can achieve this upperbound. Simulation results demonstrate the efficiency of our proposed solution and substantiate the importance of using sink mobility for energy-constrained sensor networks.
In this paper, we study the lifetime optimization problem in wireless sensor networks using mobile sink nodes. This problem is inherently difficult since we need to consider both sink scheduling and data routing. Thro...
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In this paper, we study the lifetime optimization problem in wireless sensor networks using mobile sink nodes. This problem is inherently difficult since we need to consider both sink scheduling and data routing. Through a simple case study we develop a novel notation named the Placement pattern (PP) to bound traffic patterns with candidate locations. This significantly decreases the number of elements needed to be scheduled. Based on the PP, we mathematically formulate this optimization problem as a Mixed-integer non-linear programming (MINLP), which is very tough and time consuming to solve. By proving that the problem is NP-complete, we point out that instead of seeking an optimal algorithm, heuristic algorithms, especially those with performance guarantee, would be much more desirable to develop. Furthermore, in order to help identify performance gains of heuristic algorithms proposed in the future, we develop a Linear programming (LP) formulation which serves as an upper bound by adopting a reformulation and relaxation technique.
3D models are a new kind of cross-media resource which can be frequently seen in the network. Since the amount of them is very huge now, content-based retrieval can help to recognize a certain object or retrieve simil...
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3D models are a new kind of cross-media resource which can be frequently seen in the network. Since the amount of them is very huge now, content-based retrieval can help to recognize a certain object or retrieve similar ones from the giant database. This paper presents a new method for deriving 3D moment invariants and uses them as shape descriptors for the representation of 3D models. They are insensitive to surface noise and can be used in pervasive environment conveniently. We also illustrate how to build up experimental system and simulate 3D shape retrieval in wireless environment
We address the optimal sink scheduling problem in wireless sensor networks (WSNs). The problem is inherently difficult since sink scheduling and data routing are tightly coupled. Previous approaches either have questi...
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We address the optimal sink scheduling problem in wireless sensor networks (WSNs). The problem is inherently difficult since sink scheduling and data routing are tightly coupled. Previous approaches either have questionable performance due to no joint considerations, or are based on relaxed constraints. Our aim is to fill in this blank in the research. First, by discretizing continuous time, we develop a novel bound technique to connect time-varying routes with the placement of sinks. This bounding technique transforms time-related constraints into pattern-based ones and allows us to mathematically formulate this optimization in a pattern-based way. The complexity of directly solving this optimization is intractable; therefore, on the basis of column generation (CG), a computationally efficient algorithm is developed to reduce the complexity by decomposing the problem into sub-problems and iteratively solving them to approach optimality. Simulations demonstrate the efficiency of the algorithm and substantiate the importance of sink mobility in energy-constrained sensor networks.
Delay defects under high temperature have been one of the most critical factors to affect the reliability of computersystems, and the current test methods don't address this problem properly. In this paper, a tem...
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ISBN:
(纸本)9783981537048
Delay defects under high temperature have been one of the most critical factors to affect the reliability of computersystems, and the current test methods don't address this problem properly. In this paper, a temperature-aware software-based self-testing (SBST) technique is proposed to self-heat the processors within a high temperature range and effectively test delay faults under high temperature. First, it automatically generates high-quality test programs through automatic test instruction generation (ATIG), and avoids over-testing caused by nonfunctional patterns. Second, it exploits two effective powerintensive program transformations to self-heat up the processors internally. Third, it applies a greedy algorithm to search the optimized schedule of the test templates in order to generate the test program while making sure that the temperature of the processor under test is within the specified range. Experimental results show that the generated program is successful to guarantee delay test within the given temperature range, and achieves high test performance with functional patterns.
In current convolutional neural network (CNN) accelerators, communication (i.e., memory access) dominates the energy consumption. This work provides comprehensive analysis and methodologies to minimize the communicati...
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ISBN:
(数字)9781728161495
ISBN:
(纸本)9781728161501
In current convolutional neural network (CNN) accelerators, communication (i.e., memory access) dominates the energy consumption. This work provides comprehensive analysis and methodologies to minimize the communication for CNN accelerators. For the off-chip communication, we derive the theoretical lower bound for any convolutional layer and propose a dataflow to reach the lower bound. This fundamental problem has never been solved by prior studies. The on-chip communication is minimized based on an elaborate workload and storage mapping scheme. We in addition design a communication-optimal CNN accelerator architecture. Evaluations based on the 65nm technology demonstrate that the proposed architecture nearly reaches the theoretical minimum communication in a three-level memory hierarchy and it is computation dominant. The gap between the energy efficiency of our accelerator and the theoretical best value is only 37-87%.
In current convolutional neural network (CNN) accelerators, communication (i.e., memory access) dominates the energy consumption. This work provides comprehensive analysis and methodologies to minimize the communicati...
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