In numerical computation, the inherent rounding errors of floating-point operations often affect the precision of mathematical functions. The use of high-precision achieved through software-dependent simulation for pr...
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In numerical computation, the inherent rounding errors of floating-point operations often affect the precision of mathematical functions. The use of high-precision achieved through software-dependent simulation for precision compensation may result in significant performance overhead. Error-free transformations (EFT) technology, based on hardware-supported precision to approximate high-precision implementation, can effectively balance accuracy and performance. However, enhancing the precision of mathematical functions is a very complex and challenging issue. There is a lack of relevant research on when EFT technology can be used to improve the precision of mathematical functions, what effects can be achieved, and what impact it may have on program performance. In this work, we present an empirical study on the applicability and effectiveness of using error-free transformations (EFT) in floating-point computation to assess their potential and limitations in improving precision over mathematical functions. We select 42 mathematical functions from the GNU Scientific Library (GSL), known for significant rounding errors. We evaluate the EFT techniques from three aspects: the applicability of EFT for different mathematical functions (especially at the maximum error point and its vicinity), the precision improvement of EFT in input domains near the error-triggering input, and the performance of EFT compared with the high-precision versions. Experimental results show that EFT has advantages in reducing floating-point errors across 27 functions. Furthermore, while improving the accuracy of mathematical functions within specific input ranges near the maximum error input, EFT achieves a 10.92× speedup compared to long double precision and a 2426.3× speedup compared to mpmath. These findings suggest that EFT achieves computational accuracy to the real results with much lower overhead than conventional high-precision calculations, which makes EFT a promising technology for balan
In the development of ethernet passive optical networks (EPONs), quality of service (QoS) support and fairness per optical network unit (ONU) are crucial issues. However, making an elaborate analysis of the existing p...
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Software, hardware, data, and computing power can be abstracted and encapsulated as services authorised to users in a paid or free manner for on demand deployment. Service composition combines multiple existing servic...
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Event-based computation has recently gained increasing research interest for applications of vision recogni-tion due to its intrinsic advantages on efficiency and ***,the existing event-based models for vision recogni...
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Event-based computation has recently gained increasing research interest for applications of vision recogni-tion due to its intrinsic advantages on efficiency and ***,the existing event-based models for vision recogni-tion are faced with several issues,such as large network complexity and expensive training *** this paper,we propose an improved multi-liquid state machine(M-LSM)method for high-performance vision ***,we intro-duce two methods,namely multi-state fusion and multi-liquid search,to optimize the liquid state machine(LSM).Multi-state fusion by sampling the liquid state at multiple timesteps could reserve richer spatiotemporal *** adapt network architecture search(NAS)to find the potential optimal architecture of the multi-liquid state *** also train the M-LSM through an unsupervised learning rule spike-timing dependent plasticity(STDP).Our M-LSM is evalu-ated on two event-based datasets and demonstrates state-of-the-art recognition performance with superior advantages on network complexity and training cost.
With the advent of physics informed neural networks(PINNs),deep learning has gained interest for solving nonlinear partial differential equations(PDEs)in recent *** this paper,physics informed memory networks(PIMNs)ar...
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With the advent of physics informed neural networks(PINNs),deep learning has gained interest for solving nonlinear partial differential equations(PDEs)in recent *** this paper,physics informed memory networks(PIMNs)are proposed as a new approach to solving PDEs by using physical laws and dynamic behavior of *** the fully connected structure of the PINNs,the PIMNs construct the long-term dependence of the dynamics behavior with the help of the long short-term memory ***,the PDEs residuals are approximated using difference schemes in the form of convolution filter,which avoids information loss at the neighborhood of the sampling ***,the performance of the PIMNs is assessed by solving the Kd V equation and the nonlinear Schr?dinger equation,and the effects of difference schemes,boundary conditions,network structure and mesh size on the solutions are *** show that the PIMNs are insensitive to boundary conditions and have excellent solution accuracy even with only the initial conditions.
Feature extraction of point clouds is a fundamental component of three-dimensional(3D)vision *** existing feature extraction networks primarily focus on enhancing the geometric perception abilities of networks and ove...
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Feature extraction of point clouds is a fundamental component of three-dimensional(3D)vision *** existing feature extraction networks primarily focus on enhancing the geometric perception abilities of networks and overlook the crucial role played by *** instance,though two airplane wings share the same shape,it demands distinct feature representations due to their differing *** this paper,we introduce a novel module called position aware module(PAM)to leverage the coordinate features of points for positional encoding,and integrating this encoding into the feature extraction network to provide essential positional ***,we embed PAM into the Point Net++framework,and design a novel feature extraction network,named Point Net *** validate the effectiveness of Point Net V3,we conducted comprehensive experiments including classification,object tracking and object detection on point *** results of remarkable improvement in three tasks demonstrate the exceptional performance achieved by Point Net V3 in point cloud processing.
Network-on-Chip(NoC)is widely adopted in neuromorphic processors to support communication between neurons in spiking neural networks(SNNs).However,SNNs generate enormous spiking packets due to the one-to-many traffic ...
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Network-on-Chip(NoC)is widely adopted in neuromorphic processors to support communication between neurons in spiking neural networks(SNNs).However,SNNs generate enormous spiking packets due to the one-to-many traffic *** spiking packets may cause communication pressure on *** propose a path-based multicast routing method to alleviate the ***,all destination nodes of each source node on NoC are divided into several ***,multicast paths in the clusters are created based on the Hamiltonian path *** proposed routing can reduce the length of path and balance the communication load of each ***,we design a lightweight microarchitecture of NoC,which involves a customized multicast packet and a routing *** use six datasets to verify the proposed multicast *** with unicast routing,the running time of path-based multicast routing achieves 5.1x speedup,and the number of hops and the maximum transmission latency of path-based multicast routing are reduced by 68.9%and 77.4%,*** maximum length of path is reduced by 68.3%and 67.2%compared with the dual-path(DP)and multi-path(MP)multicast routing,***,the proposed multicast routing has improved performance in terms of average latency and throughput compared with the DP or MP multicast routing.
Transcendental functions are important functions in various high performance computing *** these functions are time-consuming and the vector units on modern processors become wider and more scalable,there is an increa...
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Transcendental functions are important functions in various high performance computing *** these functions are time-consuming and the vector units on modern processors become wider and more scalable,there is an increasing demand for developing and using vector transcendental functions in such performance-hungry ***,the performance of vector transcendental functions as well as their accuracy remain largely *** address this issue,we perform a comprehensive evaluation of two Single Instruction Multiple Data(SIMD)intrinsics based vector math libraries on two ARMv8 compatible *** first design dedicated microbenchmarks that help us understand the performance behavior of vector transcendental ***,we propose a piecewise,quantitative evaluation method with a set of meaningful metrics to quantify their performance and *** analyzing the experimental results,we find that vector transcendental functions achieve good performance speedups thanks to the vectorization and algorithm ***,vector math libraries can replace scalar math libraries in many cases because of improved performance and satisfactory *** this,the implementations of vector math libraries are still immature,which means further optimization is needed,and our evaluation reveals feasible optimization solutions for future vector math libraries.
UAV networks often encounter jamming attacks, under which multi-radio protocols have to switch radios to accelerate communication recovery. However, the existing protocols rely on exchange of hello messages to detect ...
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UAV networks often encounter jamming attacks, under which multi-radio protocols have to switch radios to accelerate communication recovery. However, the existing protocols rely on exchange of hello messages to detect jamming, leading to long sensing time and thus slow routing recovery. To address the issues raised by jamming attacks, we propose a new routing protocol, Electromagnetic Spectrum situation awareness Optimized Link State Routing (ESOLSR) protocol, to improve the existing OLSRv2 protocol. ESOLSR utilizes the spectrum situation awareness capability from the physical layer, and adopts joint-updating of link status, updating of interface functions, and adaptive adjustment of parameters. Our simulation results show that the improved protocol, ESOLSR, can recover routing and resume normal communication 26.6% faster compared to the existing protocols.
The proposed study focuses on the critical issue of corrosion,which leads to significant economic losses and safety risks worldwide.A key area of emphasis is the accuracy of corrosion detection *** recent studies have...
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The proposed study focuses on the critical issue of corrosion,which leads to significant economic losses and safety risks worldwide.A key area of emphasis is the accuracy of corrosion detection *** recent studies have made progress,a common challenge is the low accuracy of existing detection *** models often struggle to reliably identify corrosion tendencies,which are crucial for minimizing industrial risks and optimizing resource *** proposed study introduces an innovative approach that significantly improves the accuracy of corrosion detection using a convolutional neural network(CNN),as well as two pretrained models,namely YOLOv8 and *** leveraging advanced technologies and methodologies,we have achieved high accuracies in identifying and managing the hazards associated with corrosion across various industrial *** advancement not only supports the overarching goals of enhancing safety and efficiency,but also sets a new benchmark for future research in the *** results demonstrate a significant improvement in the ability to detect and mitigate corrosion-related concerns,providing a more accurate and comprehensive solution for industries facing these *** CNN and EfficientNetB0 exhibited 100%accuracy,precision,recall,and F1-score,followed by YOLOv8 with respective metrics of 95%,100%,90%,and 94.74%.Our approach outperformed state-of-the-art with similar datasets and methodologies.
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