Optimizing Instruction Scheduling and Register Allocation for Register-File-Connected Clustered VLIW Architectures

Clustering has become a common trend in very long instruction words (VLIW) architecture to solve the problem of area, energy consumption, and design complexity. Register-file-connected clustered (RFCC) VLIW architecture uses the mechanism of global register file to accomplish the inter-cluster data communications, thus eliminating the performance and energy consumption penalty caused by explicit inter-cluster data move operations in traditional bus-connected clustered (BCC) VLIW architecture. However, the limit number of access ports to the global register file has become an issue which must be well addressed; otherwise the performance and energy consumption would be harmed. In this paper, we presented compiler optimization techniques for an RFCC VLIW architecture called Lily, which is designed for encryption systems. These techniques aim at optimizing performance and energy consumption for Lily architecture, through appropriate manipulation of the code generation process to maintain a better management of the accesses to the global register file. All the techniques have been implemented and evaluated. The result shows that our techniques can significantly reduce the penalty of performance and energy consumption due to access port limitation of global register file

Mechanisms of matrix metalloproteinase-9 upregulation and tissue destruction in various organs in influenza A virus infection

Severe influenza is characterized clinicopathologically by multiple organ failure, although the relationship amongst virus and host factors that influence this morbid outcome and the underlying mechanisms of action remain unclear. The present study identified marked upregulation of matrix metalloproteinase (MMP)-9 and pro-inflammatory cytokine tumor necrosis factor alpha (TNF-α) in various organs after intranasal infection of influenza A WSN virus. MMP-9 and TNF-α were upregulated in the lung, the site of initial infection, as well as in the brain and heart. The infection-induced MMP-9 upregulation was inhibited by anti-TNF-α antibodies and by anti-oxidative reagents pyrrolidine dithiocarbamate and N-acetyl-L-cysteine, which inhibit activation of nuclear factor kappa B (NF-χB), as well as by nordihydroguaiaretic acid, which inhibits activation of activator protein 1 (AP-1). In addition, MMP-9 upregulation via TNF-α was also suppressed by inhibitors of mitogen-activated protein kinases (MAPKs), such as extracellular signalregulated kinase 1/2 and p38, and partly by a c-Jun N-terminal kinase inhibitor. These results indicated that the influenza-induced MMP-9 upregulation in various organs is mediated through MAPK-NF-χB- and/or AP-1-dependent mechanisms. Strategies that neutralize TNF-α as well as inhibitors of MAPK-NF-χB- and/or AP-1-dependent pathways may be useful for suppressing the MMP-9 effect and thus preventing multiple organ failure in severe influenza

Ergodic Rate and Outage Performance of Full-Duplex NOMA Relaying with Channel Estimation Errors and Low-Resolution ADCs

In this paper, we analyze the performance of a full-duplex (FD) cooperative non-orthogonal multiple access (C-NOMA) relaying system with an amplify-and-forward (AF) protocol in the presence of loopback interference in FD transceivers. Particularly, by considering channel estimation errors and quantization noise in low-resolution analog-to-digital converters (ADCs), the accurate approximation expression for the ergodic rate and closed-form solution for the outage probability are derived, respectively. The validity of the theoretical results is verified by Monte Carlo simulations, which show that both channel estimation errors and quantization noise have deleterious effects on ergodic rate and outage performance for moderate and high signal-to-noise ratios (SNR). In the second phase of the C-NOMA system, both the outage performance and ergodic sum rate decrease at high SNRs due to the effects of loop interference. When the ADC dynamic range reaches a certain level, the system performance is more affected by loopback interference and channel estimation errors compared to the quantization noise of the ADCs

Blind Orthogonal Least Squares based Compressive Spectrum Sensing

Compressive spectrum sensing (CSS) has been widely studied in wideband cognitive radios, benefiting from the reduction of sampling rate via compressive sensing (CS) technology. However, the sensing performance of most existing CSS excessively relies on the prior information such as spectrum sparsity or noise variance. Thus, a key challenge in practical CSS is how to work effectively even in the absence of such information. In this paper, we propose a blind orthogonal least squares based CSS algorithm (B-OLS-CSS), which functions properly without the requirement of prior information. Specifically, we develop a novel blind stopping rule for the OLS algorithm based on its probabilistic recovery condition. This innovative rule gets rid of the need of the spectrum sparsity or noise information, but only requires the computational-feasible mutual incoherence property of the given measurement matrix. Our theoretical analysis indicates that the signal-to-noise ratio required by the proposed B-OLS-CSS for achieving a certain sensing accuracy is relaxed than that by the benchmark CSS using the OMP algorithm, which is verified by extensive simulation results.Comment: 5 figures, submitted to IEEE Transactions on Vehicular Technology for possible publicatio