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Smart surfaces as one of the promising enabling technologies for sixth-generation wireless communications (6G) are paving their way. In this article, aiming at radio frequency electromagnetic field compliance assessments of them, two approximate approaches, namely the array-theory approach and the equivalent-current approach, are investigated. Smart surfaces with different beams, sizes, and shapes are designed, assessed, and analyzed. The results are compared with those of a reference approach, which is based on full-wave simulations, in terms of assessment accuracy. On one hand, both the approximate approaches provide significant improvement in assessment time, which is quite useful for compliance assessments in exposure scenarios with large numbers of possible beam states. On the other hand, it is found that the assessment accuracy for the two approximate approaches depends intrinsically on system configurations. Specifically, for a circle-shaped smart surface with a diameter equaling 21 times of the element periodicity, except for extreme situations where scanning angle is far from broadside, overestimated front compliance distances with relative errors no more than 30% and 24% are obtained for the array-theory approach and the equivalent-current approach, respectively. [ABSTRACT FROM AUTHOR]

The RRAM-based accelerators have become very popular candidates for neural network acceleration due to they perform matrix-vector multiplication in-memory with high storage density and low latency. Many related works have used fixed-precision quantization to achieve the model compression and enhance the tolerance of process variation, but these methods still suffer a large accuracy degradation and poor robustness to nonideal effects. In this work, we propose a crossbar-aware mixed-precision quantization scheme, which enables to search for the optimal precision of each part of the network as a way to improve the accuracy and robustness to noise. First, we introduce a group quantization strategy that can flexibly adjust the group size dynamically according to the crossbar size. Then, we propose a detailed mixed-precision search flow to search for the optimal precision set of the network. Finally, we give a noise injection adaption training method to enhance the tolerance of noise. Experimental results show that our proposed method can improve inference accuracy by at least 2.04% compared to the fixed-precision quantization under the same resource cost. The searched architecture with the highest accuracy most accurate (MA) could achieve an accuracy of 92.39% and a resource saving of 93.30% compared to the full precision model. The searched architecture with the biggest resource savings most efficient (ME) could achieve an accuracy of 91.11% and a resource saving of 95.57% compared to the full precision model. And, the average precision of the ME mixed-precision architecture is only 1.4 bits. Besides, the results show the mixed-precision network with noise adaption training is more robust to noise than the fixed-precision network with noise adaption training. [ABSTRACT FROM AUTHOR]

The scalability issue hinders the deployment of Software-Defined Networking (SDN) in the Wide Area Networks (WANs). Existing solutions have two issues: (1) network performance relies on complicated controller synchronization, which increases the complexity of network control; (2) fine-grained flow processing enables flexible flow control at the cost of high processing load on the controllers and high flow table occupancy on switches. In this paper, we propose a scalable routing solution named HybridFlow, which achieves a good load balancing performance using a single controller with low control overhead (i.e., flow routing and rerouting overhead). HybridFlow mainly employs two techniques: hybrid routing and crucial flow rerouting. Hybrid routing enabled by commercial SDN switches gives us opportunities to reduce the processing load of the controller by routing flows with the hybrid OpenFlow/OSPF mode. Thus, the majority of flows can be routed by OSPF without involving the controller. Crucial flow rerouting realizes load balancing by dynamically identifying crucial flows based on a new metric called Variation Slope and rerouting these flows with the hybrid OpenFlow/OSPF mode. The simulation based on the real traffic traces and network typologies shows that compared with the optimal solution, HybridFlow can achieve 87% of the optimal load balancing performance by rerouting 36% less flows on average. [ABSTRACT FROM AUTHOR]