Static random access memory (SRAM) on field programmable gate arrays (FPGAs) can be emulated to offer ternary content addressable memory (TCAM) functionality. However, SRAM-based TCAM wastes storage resources. This is...
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Static random access memory (SRAM) on field programmable gate arrays (FPGAs) can be emulated to offer ternary content addressable memory (TCAM) functionality. However, SRAM-based TCAM wastes storage resources. This is due to the limited capacity of the physical addresses in the SRAM unit. This work proposes a LUTRAM-based TACM scheme on the FPGA called Memory-Efficient TCAM (ME-TCAM). METCAM divides SRAM unit into multiple virtual blocks mapping to a portion of the TCAM table to store the more address information of the TCAM table. Operation on SRAM block means that increasing the overall emulated TCAM bits/SRAM. Moreover, ME-TCAM exploits Xilinx primitives to conFigure lookup tables (LUTs) as 32 × 2 lookup table RAMs (LUTRAMs). We implement ME-TCAM using LUTRAM with a size of 512 × 48 and 1024 × 144 on a Virtex-7 FPGA device. Compared with the state-of-the-art research DUR, ME-TCAM achieves at least 2.6 times more memory efficiency.
The proceedings contain 298 papers. The topics discussed include: a paradigm of sixth sense: finger cursor;distributed model predictive control based formation tracking for differential drive robots;parallel approach ...
ISBN:
(纸本)9781728102832
The proceedings contain 298 papers. The topics discussed include: a paradigm of sixth sense: finger cursor;distributed model predictive control based formation tracking for differential drive robots;parallel approach for document representation using dictionary learning;a comparison study of optimization based pi controller tuning for PQ improvement in DSTATCOM;broad phoneme classification - a study;deliberation of robotic services to human kind using FPGA based robot;power factor monitoring and improvement using Raspberry Pi;submodule clustering based on latent representation tensor;a novel technique for biometric data protection in remote authentication system;and image encryption and reversible watermarking on quad and non-quad channel images.
Recently, due to the limitations in using cloud computing services for the recent advances IoTs applications, a newly distributedcomputing architecture is established called cloud-fog paradigm by exploiting the coope...
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ISBN:
(纸本)9781450372831
Recently, due to the limitations in using cloud computing services for the recent advances IoTs applications, a newly distributedcomputing architecture is established called cloud-fog paradigm by exploiting the cooperation between fog and cloud entities. Fog nodes are used to reduce monetary cost and transferring latency for cloud resources, while for offloading of large-scale applications cloud servers are used. In this paradigm, The main problem is task allocation which aims to select the optimal nodes among cloud and fog nodes for each task to minimize makespan, monetary and energy costs. In this paper, to solve this problem a new task allocation approach called bipartite graph with fuzzy clustering task allocation approach is proposed and it uses a hybrid DAG for representing independent and dependent tasks. Also, it uses fuzzy clustering and bipartite graph to solve the uncertainty executing problem and find the maximum bipartite matching, respectively. The conducted simulation results show that the proposed approach can achieve a higher performance int terms of makespan, total cost, and cost-makespan tradeoff than existing approaches.
Many of the computer data centres across world are interconnected of network systems. In the network connection, the distributed systems of multiprocessors are arranged for time-dependent run of tasks through task sch...
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Clustering algorithms are an important part of unsupervised machine learning. With Big Data, applying clustering algorithms such as KMeans has become a challenge due to the significantly larger volume of data and the ...
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To enable the systematic evaluation of complex technical systems by engineers from various disciplines advanced 3D simulation environments that model all relevant aspects are used. In these Virtual Testbeds real-time ...
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This paper proposes an effective fast Fourier transform (FFT) processor for 1024-point computation based on the radix-2 of decimation-in-frequency (R2DIF) and uses the pipelined feedback (PF) technique via shift regis...
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ISBN:
(纸本)9789811303418;9789811303401
This paper proposes an effective fast Fourier transform (FFT) processor for 1024-point computation based on the radix-2 of decimation-in-frequency (R2DIF) and uses the pipelined feedback (PF) technique via shift registers to efficiently share the same storage between the inputs and outputs during computation. The large memory footprint of the complex twiddle factor multipliers, and hence, area on a chip, of the proposed design is reduced by employing the coordinate rotation digital computer (CoRDiC), which replaces the complex multipliers and does not require memory blocks to store the twiddle factors. To enhance the efficient usage of the hardware resources, the proposed design only uses distributed logic. This can eliminate the use of dedicated functional blocks, which are usually limited to the target chip. The entire proposed system is mapped on a Virtex-7 field-programmable gate array (FPGA) for functional verification and synthesis. The achieved result is the proposed FFT processor more effective in terms of the speed, precision, and resource, as shown in experimental results.
Network virtualization recognized as an enabling technology for the forthcoming networks is utterly popular. One of the main challenges of network virtualization is called the virtual network embedding problem. Virtua...
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ISBN:
(数字)9781728185262
ISBN:
(纸本)9781728185279
Network virtualization recognized as an enabling technology for the forthcoming networks is utterly popular. One of the main challenges of network virtualization is called the virtual network embedding problem. Virtual network embedding (VNE) aims to allocate a set of virtual machines onto a set of interconnected physical hardware in the cloud computing environment. Traditional exact solutions, considered as a time-consuming process to achieve a global optimal solution, have been proofed to be NP-hard. On the other hand, some existing heuristic solutions tend to decouple VNE problems into two stages: virtual node mapping (VNoM) and virtual link mapping (VLiM). Undoubtedly, these kinds of decomposition would result in low acceptance ratio and inefficient substrate resource utilization. In this paper, we propose a distributedparallel Genetic Algorithm combined with graph theory for solving VNE in one-stage. Our proposed algorithm achieves better performance than previous baseline solutions while meeting the stringent time requirements for online VNE problems.
The success of Deep Learning (DL) algorithms in computer vision tasks have created an on-going demand of dedicated hardware architectures that could keep up with the their required computation and memory complexities....
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ISBN:
(纸本)9781450371896
The success of Deep Learning (DL) algorithms in computer vision tasks have created an on-going demand of dedicated hardware architectures that could keep up with the their required computation and memory complexities. This task is particularly challenging when embedded smart camera platforms have constrained resources such as power consumption, Processing Element (PE) and communication. This article describes a heterogeneous system embedding an FPGA and a GPU for executing CNN inference for computer vision applications. The built system addresses some challenges of embedded CNN such as task and data partitioning, and workload balancing. The selected heterogeneous platform embeds an Nvidia (R) Jetson TX2 for the CPU-GPU side and an Intel Altera (R) Cyclone10GX for the FPGA side interconnected by PCIe Gen2 with a MIPI-CSI camera for prototyping. This test environment will be used as a support for future work on a methodology for optimized model partitioning.
The feature selection effect directly affects the classification accuracy of the text. This paper introduces a new text feature selection method based on bat optimization. This method uses the traditional feature sele...
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ISBN:
(纸本)9781728140698
The feature selection effect directly affects the classification accuracy of the text. This paper introduces a new text feature selection method based on bat optimization. This method uses the traditional feature selection method to pre-select the original features, and then uses the bat group algorithm to optimize the pre-selected features in binary code form, and uses the classification accuracy as the individual fitness. However, when the amount of text information is large, the execution time of the single machine is long. According to this shortcoming, combining the Bat Algorithm and the Spark parallelcomputing framework, the text feature selection algorithm SBATFS is proposed. The algorithm combines the good search performance of the bat algorithm with the distributed and efficient calculation speed to realize the efficient solution of the text feature selection optimization model. The results show that compared with the traditional feature selection method, after SBATFS is used for feature optimization, the classification accuracy is effectively improved.
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