The exploration of component states for optimizing maintenance schedules in complex systems has garnered significant interest from researchers.However,current literature usually overlooks the critical aspects of system flexibility and reconfigurability.Judicious implementation of system reconfiguration can effectively mitigate system downtime and enhance production continuity.This study proposes a dynamic condition-based maintenance policy considering reconfiguration for reconfigurable systems.A double-layer decision rule was constructed for the devices and systems.To achieve the best overall maintenance effect of the system,the remaining useful life probability distribution and recommended maintenance time of each device were used to optimize the concurrent maintenance time window of the devices and determine whether to reconfigure them.A comprehensive maintenance efficiency index was introduced that simultaneously considered the maintenance cost rate,reliability,and availability of the system to characterize the overall maintenance effect.The reconfiguration cost was included in the maintenance cost.The proposed policy was tested through numerical experiments and compared with different-level policies.The results show that the proposed policy can significantly reduce the downtime and maintenance costs and improve the overall system reliability and availability.
Due to the diversity of graph computing applications, the power-law distribution of graph data, and the high compute-to-memory ratio, traditional architectures face significant challenges regarding poor flexibility, imbalanced workload distribution, and inefficient memory access when executing graph computing tasks. Graph computing accelerator, GraphApp, based on a reconfigurable processing element(PE) array was proposed to address the challenges above. GraphApp utilizes 16 reconfigurable PEs for parallel computation and employs tiled data. By reasonably dividing the data into tiles, load balancing is achieved and the overall efficiency of parallel computation is enhanced. Additionally, it preprocesses graph data using the compressed sparse columns independently(CSCI) data compression format to alleviate the issue of low memory access efficiency caused by the high memory access-to-computation ratio. Lastly, GraphApp is evaluated using triangle counting(TC) and depth-first search(DFS) algorithms. Performance analysis is conducted by measuring the execution time of these algorithms in GraphApp against existing typical graph frameworks, Ligra, and GraphBIG, using six datasets from the Stanford Network Analysis Project(SNAP) database. The results show that GraphApp achieves a maximum performance improvement of 30.86% compared to Ligra and 20.43% compared to GraphBIG when processing the same datasets.
Deng JunyongJia YantingZhang BaoxiangKang YuchunLu Songtao
In the case of massive data,matrix operations are very computationally intensive,and the memory limitation in standalone mode leads to the system inefficiencies.At the same time,it is difficult for matrix operations to achieve flexible switching between different requirements when implemented in hardware.To address this problem,this paper proposes a matrix operation accelerator based on reconfigurable arrays in the context of the application of recommender systems(RS).Based on the reconfigurable array processor(APR-16)with reconfiguration,a parallelized design of matrix operations on processing element(PE)array is realized with flexibility.The experimental results show that,compared with the proposed central processing unit(CPU)and graphics processing unit(GPU)hybrid implementation matrix multiplication framework,the energy efficiency ratio of the accelerator proposed in this paper is improved by about 35×.Compared with blocked alternating least squares(BALS),its the energy efficiency ratio has been accelerated by about 1×,and the switching of matrix factorization(MF)schemes suitable for different sparsity can be realized.
As part of the 4th industrial revolution,programmable mechanical metamaterials exhibit great application potential in flexible robotics,vibration control,and impact protection.However,maintaining a programmed state without sustaining the external stimulus is often challenging and leads to additional energy consumption.Inspired by Rubik’s cube,we design and study an in-situ programmable and distribution-reconfigurable mechanical metamaterial(IPDR-MM).A matrix model is developed to model IPDR-MMs and describe their morphological transitions.Based on this model,the reinforcement learning method is employed to find the pathways for morphological transitions.We find that IPDR-MMs have controllable stiffness across several orders of magnitude and a wide range of adjustable anisotropies through morphology transformation.Additionally,because of the independence of the directions of morphology transformation and bearing,IPDR-MMs exhibit good stability in bearing and can readily achieve high stiffness.The Rubik’s cube-inspired design concept is also instructive for other deformable structures and metamaterials,and the current version of the proposal should be sufficiently illustrative to attract and broaden interdisciplinary interests.
Millimeter wave with large bandwidth,high transmission rate,and low delay is considered a reliable alternative to cope with the spectrum shortage.However,the fast attenuation and narrow beam characteristics make it difficult to achieve long-distance or wide-range applications.Here,a 1-bit dual-band reflective reconfigurable intelligent surface(RIS)for signal enhancement in millimeter wave with 16×16 elements is designed,fabricated,and measured.Different from most existent RIS,dynamic programming is realized at two separate frequency bands by integrating the PIN diodes and field-programmable gate array(FPGA).Particularly,the beam deflection,dual-beam,and multi-beam are created based on the coding theory and convolution operation,proving the effectiveness of wavefront manipulation.Moreover,the far-field patterns and signal power with different coding sequences are measured and compared.It is indicated that the received signal power is 6–7 dB stronger than that without coding,which shows good agreement with the desired expectations.The proposed reconfigurable metasurface exhibits great potential in beam forming,making it a promising candidate for progressive wireless communication applications.
YAO HuiMingDU XinRunWANG HengBinGAO SongPANG YaChenZHENG HaiRongGONG HuiWenLI YiXU JianChunZHANG JianHuaBI Ke
As the number of cores in a multicore system increases,the communication pressure on the interconnection network also increases.The network-on-chip(NoC)architecture is expected to take on the ever-expanding communication demands triggered by the ever-increasing number of cores.The communication behavior of the NoC architecture exhibits significant spatial–temporal variation,posing a considerable challenge for NoC reconfiguration.In this paper,we propose a traffic-oriented reconfigurable NoC with augmented inter-port buffer sharing to adapt to the varying traffic flows with a high flexibility.First,a modified input port is introduced to support buffer sharing between adjacent ports.Specifically,the modified input port can be dynamically reconfigured to react to on-demand traffic.Second,it is ascertained that a centralized output-oriented buffer management works well with the reconfigurable input ports.Finally,this reconfiguration method can be implemented with a low overhead hardware design without imposing a great burden on the system implementation.The experimental results show that compared to other proposals,the proposed NoC architecture can greatly reduce the packet latency and improve the saturation throughput,without incurring significant area and power overhead.
