As power consumption of the clock tree dominates over 40% of the total power in modern high performance VLSI designs, measures must be taken to keep it under control. One of the most effective methods is based on clock gating to shut off the clock when the modules are idle. However, previous works on gated clock tree power minimization are mostly focused on clock routing and the improvements are often limited by the given registers placement. The purpose of this work is to navigate the registers during placement to further reduce the clock tree power based on clock gating. Our method performs activity-aware register clustering that reduces the clock tree power not only by clumping the registers into a smaller area, but also by pulling the registers with the similar activity patterns closely to shut off the clock more time for the resultant subtrees. In order to reduce the impact of signal nets wirelength and power due to register clustering, we apply the timing and activity based net weighting in [14], which reduces the nets switching power by assigning a combination of activity and timing weights to the nets with higher switching rates or more critical timing. To tradeoff the power dissipated by the clock tree and the control signal, we extend the idea of local ungating in [6] and propose an algorithm of gate control signal optimization, which still sets the gate enable signal high if a register is active for a number of consecutive clock cycles. Experimental results on a set of MCNC benchmarks show that our approach is able to reduce the power and total wirelength of clock tree greatly with minimal overheads.

This paper presents a hybrid technique combining the mode-matching method and moment method to analyze various slots cut in the wall of a rectangular waveguide partially filled with a dielectric slab. The waveguide slot structure is decomposed into two parts: a dielectric-loaded waveguide T-junction and an open-ended waveguide radiating into half space. The T-junction is analyzed by the mode-matching method, while the open-ended waveguide is characterized by the moment method with the modal functions in the slot being the full domain basis functions. A new approach for computing multidimensional integrals is proposed in the formulation of the open-ended waveguide, which greatly reduces the computation effort. The T-junction and the open-ended waveguide are then cascaded to obtain the final scattering parameters of the slot structure. Numerical results for different slots on a dielectric-loaded rectangular waveguide calculated by the hybrid method are presented and validated by comparing with measured and simulated data by Ansoft's HFSS. Good agreement is observed for all the cases considered. Parametrical studies are also conducted to examine the effect of the dielectric slab's thickness and relative permittivity on slot antenna's impedance/admittance.

Reliable wireless communication often requires accurate knowledge of the underlying multipath channels. Numerous measurement campaigns have shown that physical multipath channels tend to exhibit a sparse structure. Conventional blind channel identification (BCI) strategies such as the least squares, which are known to be optimal under the assumption of rich multipath channels, are ill-suited to exploiting the inherent sparse nature of multipath channels. Recently, l1-norm regularized least-squares-type approaches have been proposed to address this problem with a single parameter governing all coefficients, which is equivalent to maximum a posteriori probability estimation with a Laplacian prior for the channel coefficients. Since Laplace prior is not conjugate to the Gaussian likelihood, no closed form of Bayesian inference is possible. Following a different approach, this paper deals with blind channel identification of a single-input multiple-output (SIMO) system based on sparse Bayesian learning (SBL). The inherent sparse nature of wireless multipath channels is exploited by incorporating a transformative cross relation formulation into a general Bayesian framework, in which the filter coefficients are governed by independent scalar parameters. A fast iterative Bayesian inference method is then applied to the proposed model for obtaining sparse solutions, which completely eliminates the need for computationally costly parameter fine tuning, which is necessary in the l1-norm regularization method. Simulation results are provided to demonstrate the superior effectiveness of the proposed channel estimation algorithm over the conventional least squares (LS) scheme as well as the l1-norm regularization method. It is shown that the proposed algorithm exhibits superior estimation performance compared to both LS and l1-norm regularization methods.

This paper presents a modal-expansion analysis of the electromagnetically coupled coaxial dipole antenna. The analysis of the antenna problem is initially simplified using the even-odd mode excitation and then the resultant half structure is divided into two parts; one is the characterization of a coaxial feeding network and the other is the modeling of a sleeve monopole antenna driven by a coaxial line. The formally exact modal-expansion method is employed to analyze both parts. The analysis of the sleeve monopole antenna is facilitated by introducing a perfectly conducting boundary at a distance from the monopole's top end. The current distribution and input impedance of the electromagnetically coupled coaxial dipole antenna are obtained by finding expansion coefficients through enforcing the continuity of tangential field components across regional interfaces and cascading the two parts together. Numerical results for the coaxial dipole antenna's radiation characteristics are presented and discussed.

Visual Question Answering (VQA) is multi-task research that requires simultaneous processing of vision and text. Recent research on the VQA models employ a co-attention mechanism to build a model between the context and the image. However, the features of questions and the modeling of the image region force irrelevant information to be calculated in the model, thus affecting the performance. This paper proposes a novel dual self-guided attention with sparse question networks (DSSQN) to address this issue. The aim is to avoid having irrelevant information calculated into the model when modeling the internal dependencies on both the question and image. Simultaneously, it overcomes the coarse interaction between sparse question features and image features. First, the sparse question self-attention (SQSA) unit in the encoder calculates the feature with the highest weight. From the self-attention learning of question words, the question features of larger weights are reserved. Secondly, sparse question features are utilized to guide the focus on image features to obtain fine-grained image features, and to also prevent irrelevant information from being calculated into the model. A dual self-guided attention (DSGA) unit is designed to improve modal interaction between questions and images. Third, the sparse question self-attention of the parameter δ is optimized to select these question-related object regions. Our experiments with VQA 2.0 benchmark datasets demonstrate that DSSQN outperforms the state-of-the-art methods. For example, the accuracy of our proposed model on the test-dev and test-std is 71.03% and 71.37%, respectively. In addition, we show through visualization results that our model can pay more attention to important features than other advanced models. At the same time, we also hope that it can promote the development of VQA in the field of artificial intelligence (AI).