Power estimation of superscalar microprocessor using VHDL model

Power optimization becomes more and more important due to the design cost and reliability. Sometimes high power consumption means expensive package cost and low reliability. The first step in optimizing power consumption is determining where power is consumed within a processor. While system-level code tracing and bit transition calculation are not enough to estimate the power distribution, transistor-level HSPICE simulation to model a microprocessor is too complex and time-consuming. In our research, a VHDL model with enhanced signal tracing function will be developed based on the existing VHDL behavior model. The power consumption of superscalar microprocessor in terms of switching activity and capacitance will be carefully studied. Two factors served as the basis for study: accessibility and importance for power calculations. A brief examination of the datapath suggests that the register file, the instruction cache and data cache are some of the major contributors to power usage. It was therefore decided to track the input and output bit transitions to these modules. These transitions are counted along with the number of accesses to each of the modules. In order to gather all of this data, the original VHDL model simulator has been enhanced. As instructions pass through the CPU, additional code is required to track and record the necessary information. For each individual instruction in the ISA, various information is recorded based on the elements in the processor that the instruction affects. For instance, if the simulator is about to execute a load instruction, the instruction uses the programmer counter, the instruction bus, data bus, the address bus, the ALU (adder) and the register file. The information being recorded for each of these elements must be updated to reflect the execution of that particular load instruction. Also, the inside circuit of each module, i.e. register file, instruction cache and data cache and the 6-transistor memory cell layout considering the 0.25μm CMOS technology will be studied in order to extract the capacitance. We do not need very accurate, absolute power estimation, therefore, we will try to keep the model simple

Tackling Non-Stationarity in Reinforcement Learning via Causal-Origin Representation

In real-world scenarios, the application of reinforcement learning is significantly challenged by complex non-stationarity. Most existing methods attempt to model changes in the environment explicitly, often requiring impractical prior knowledge. In this paper, we propose a new perspective, positing that non-stationarity can propagate and accumulate through complex causal relationships during state transitions, thereby compounding its sophistication and affecting policy learning. We believe that this challenge can be more effectively addressed by tracing the causal origin of non-stationarity. To this end, we introduce the Causal-Origin REPresentation (COREP) algorithm. COREP primarily employs a guided updating mechanism to learn a stable graph representation for states termed as causal-origin representation. By leveraging this representation, the learned policy exhibits impressive resilience to non-stationarity. We supplement our approach with a theoretical analysis grounded in the causal interpretation for non-stationary reinforcement learning, advocating for the validity of the causal-origin representation. Experimental results further demonstrate the superior performance of COREP over existing methods in tackling non-stationarity

An Advanced Quantum-Resistant Signature Scheme for Cloud Based on Eisenstein Ring

The authors wish to express their appreciation to the reviewers for their helpful suggestions which greatly improved the presentation of this paper. This work was supported by the Major Program of National Natural Science Foundation of China (11290141).Peer reviewe

A Virtual Motion Camouflage Approach for Cooperative Trajectory Planning of Multiple UCAVs

This paper investigates cooperative trajectory planning of multiple unmanned combat aerial vehicles (multi-UCAV) in performing autonomous cooperative air-to-ground target attack missions. By integrating an approximate allowable attack region model, several constraint models, and a multicriterion objective function, the problem is formulated as a cooperative trajectory optimal control problem (CTOCP). Then, a virtual motion camouflage (VMC) for cooperative trajectory planning of multi-UCAV, combining with the differential flatness theory, Gauss pseudospectral method (GPM), and nonlinear programming, is designed to solve the CTOCP. In particular, the notion of the virtual time is introduced to the VMC problem formulation to handle the temporal cooperative constraints. The simulation experiments validate that the CTOCP can be effectively solved by the cooperative trajectory planning algorithm based on VMC which integrates the spatial and temporal constraints on the trajectory level, and the comparative experiments illustrate that VMC based algorithm is more efficient than GPM based direct collocation method in tackling the CTOCP

miR-499-5p Attenuates Mitochondrial Fission and Cell Apoptosis via p21 in Doxorubicin Cardiotoxicity

Doxorubicin (DOX) is a broad-spectrum anti-tumor drug, but its cardiotoxicity limits its clinical application. A better understanding of the molecular mechanisms underlying DOX cardiotoxicity will benefit clinical practice and remedy heart failure. Our present study observed that DOX caused cardiomyocyte (H9c2) apoptosis via the induction of abnormal mitochondrial fission. Notably, the expression levels of p21 increased in DOX-treated cardiomyocytes, and the silencing of p21 using siRNA greatly attenuated mitochondrial fission and apoptosis in cardiomyocytes. We also found that miR-499-5p could directly target p21 and attenuated DOX-induced mitochondrial fission and apoptosis. The role of the miR-499-5p-p21 axis in the prevention of DOX cardiotoxicity was also validated in the mice model. DOX treatment induced an upregulation of p21, which induced subsequent abnormal mitochondrial fission and myocardial apoptosis in mouse heart. Adenovirus-harboring miR-499-5p-overexpressing mice exhibited significantly reduced p21 expression, mitochondrial fission and myocardial apoptosis in hearts following DOX administration. The miR-499-5p-overexpressing mice also exhibited improved cardiomyocyte hypertrophy and cardiac function after DOX treatment. However, miR-499-5p was not involved in the DOX-induced apoptosis of cancer cells. Taken together, these findings reveal an emerging role of p21 in the regulation of mitochondrial fission program. miR-499-5p attenuated mitochondrial fission and DOX cardiotoxicity via the targeting of p21. These results provide new evidence for the miR-499-5p-p21 axis in the attenuation of DOX cardiotoxicity. The development of new therapeutic strategies based on the miR-499-5p-p21 axis is a promising path to overcome DOX cardiotoxicity as a chemotherapy for cancer treatment

Patterns and mechanisms of coseismic and postseismic slips of the 2011 M W 7.1 Van (Turkey) earthquake revealed by multi-platform synthetic aperture radar interferometry

On 23rd October 2011, a MW 7.1 reverse slip earthquake occurred in the Bardakçı-Saray thrust fault zone in the Van region, Eastern Turkey. Earlier geodetic studies have found different slip distributions in terms of both magnitude and pattern. In this paper, we present several COSMO-SkyMED (CSK), Envisat ASAR and RADARSAT-2 interferograms spanning different time intervals, showing that significant postseismic signals can be observed in the first three days after the mainshock. Using observations that combine coseismic and postseismic signals is shown to significantly underestimate coseismic slip. We hence employed the CSK pair with the minimum postseismic signals to generate one conventional interferogram and one along-track interferogram for further coseismic modelling. Our best-fit coseismic slip model suggests that: (1) this event is associated with a buried NNW dipping fault with a preferable dip angle of 49° and a maximum slip of 6.5 m at a depth of 12 km; and (2) two unequal asperities can be observed, consistent with previous seismic solutions. Significant oblique aseismic slip with predominant left-lateral slip components above the coseismic rupture zone within the first 3 days after the mainshock is also revealed by a postseismic CSK interferogram, indicating that the greatest principal stress axis might have rotated due to a significant stress drop during the coseismic rupture

A New Fusion Fault Diagnosis Method for Fiber Optic Gyroscopes

The fiber optic gyroscope (FOG) is a high precision inertial navigation device, and it is necessary to ensure its reliability for effective use. However, the extracted fault features are easily distorted due to the interference of vibrations when the FOG is in operation. In order to minimize the influence of vibrations to the greatest extent, a fusion diagnosis method was proposed in this paper. It extracted features from fault data with Fast Fourier Transform (FFT) and wavelet packet decomposition (WPD), and built a strong diagnostic classifier with a sparse auto encoder (SAE) and a neural network (NN). Then, a fusion neural network model was established based on the diagnostic output probabilities of the two primary classifiers, which improved the diagnostic accuracy and the anti-vibration capability. Then, five fault types of the FOG under random vibration conditions were established. Fault data sets were collected and generated for experimental comparison with other methods. The results showed that the proposed fusion fault diagnosis method could perform effective and robust fault diagnosis for the FOG under vibration conditions with a high diagnostic accuracy

A New Fusion Fault Diagnosis Method for Fiber Optic Gyroscopes

The fiber optic gyroscope (FOG) is a high precision inertial navigation device, and it is necessary to ensure its reliability for effective use. However, the extracted fault features are easily distorted due to the interference of vibrations when the FOG is in operation. In order to minimize the influence of vibrations to the greatest extent, a fusion diagnosis method was proposed in this paper. It extracted features from fault data with Fast Fourier Transform (FFT) and wavelet packet decomposition (WPD), and built a strong diagnostic classifier with a sparse auto encoder (SAE) and a neural network (NN). Then, a fusion neural network model was established based on the diagnostic output probabilities of the two primary classifiers, which improved the diagnostic accuracy and the anti-vibration capability. Then, five fault types of the FOG under random vibration conditions were established. Fault data sets were collected and generated for experimental comparison with other methods. The results showed that the proposed fusion fault diagnosis method could perform effective and robust fault diagnosis for the FOG under vibration conditions with a high diagnostic accuracy