A dense wavelength division multiplexing (DWDM) system improves the capacity of an optical communication system. On the other hand, nonlinear effects are critical issues that limit the performance of the DWDM system. ...
详细信息
In digital signal processing (DSP) applications, the multiplicative accumulator (MAC) process serves as the primary cognitive kernel. The MAC unit, which is constantly on the critical route, determines the overall sys...
详细信息
ISBN:
(纸本)9789819720309
In digital signal processing (DSP) applications, the multiplicative accumulator (MAC) process serves as the primary cognitive kernel. The MAC unit, which is constantly on the critical route, determines the overall system speed. A high-speed MAC is a crucial step for real-time DSP applications. A system that requires the least amount of electricity will almost probably dominate the worldwide market because of the ongoing need for small, portable devices. As a result, the development of a MAC unit with low power consumption is essential. Scientists have made several attempts to develop MAC architecture with great computation efficiency and minimal power utilization. Convolutional neural networks (CNNs) are highly effective for image, voice, and video processing but they require a lot of processing power and memory bandwidth. To solve this, hardware accelerators with plenty of multiply-accumulate (MAC) units have been proposed. However, these units increase integrated circuit (IC) gate count and power consumption due to their large multipliers. Weight-sharing accelerators compress trained CNN weight values into bins, reducing gate count and power consumption. The parallel accumulator with hybrid adder structure presented in the model is modified to use an alternative technique that involves changing the MAC units to add up each weight’s frequency and grouping the results into bins. The actual value accumulation takes place during the working multiplication phase, which considerably reduces the CNN’s gate count and power consumption. The proposed approach leverages the advantages of Wallace tree-based arithmetic units and hybridizes them with other efficient hardware structures, resulting in a highly optimized architecture for CNN layers. By carefully balancing resource utilization and performance, this approach enables the acceleration of CNN layer computations while efficiently utilizing FPGA resources. Experimental results demonstrate that the hybrid Wallace tree app
Effective resource allocation can exploit the advantage of intelligent reflective surface(IRS)assisted mobile edge computing(MEC)***,it is challenging to balance the limited energy of MTs and the strict delay requirem...
详细信息
Effective resource allocation can exploit the advantage of intelligent reflective surface(IRS)assisted mobile edge computing(MEC)***,it is challenging to balance the limited energy of MTs and the strict delay requirement of their *** this paper,in order to tackle the challenge,we jointly optimize the offloading delay and energy consumption of mobile terminals(MTs)to realize the delay-energy tradeoff in an IRS-assisted MEC network,in which non-orthogonal multiple access(NOMA)and multiantenna are applied to improve spectral *** achieve the optimal delay-energy tradeoff,an offloading cost minimization model is proposed,in which the edge computing resource allocation,signal detecting vector,uplink transmission power,and IRS phase shift coefficient are needed to be jointly *** optimization of the model is a multi-level fractional problem in complex fields with some coupled high dimension *** solve the intractable problem,we decouple the original problem into a computing subproblem and a wireless transmission subproblem based on the uncoupled relationship between different variable *** computing subproblem is proved convex and the closed-form solution is obtained for the edge computing resource ***,the wireless transmission subproblem is solved iteratively through decoupling the residual *** each iteration,the closed-form solution of residual variables is obtained through different successive convex approximation(SCA)*** verify the proposed algorithm can converge to an optimum with polynomial *** results indicate the proposed method achieves average saved costs of 65.64%,11.24%,and 9.49%over three benchmark methods respectively.
This letter introduces a novel dual-band decoupling concept using capacitor-loaded shorting posts as modal tuners to align the resonances of TM02 and TM12 with TM01 and TM11, respectively. Leveraging the modal superpo...
详细信息
This research presents a Transformer-based multi-modal architecture for predicting box office revenue by integrating diverse data sources: text, visuals, and numerical features. The proposed framework leverages RoBERT...
详细信息
Low earth orbit(LEO) satellites with wide coverage can carry mobile edge computing(MEC)servers with computing power to form the LEO satellite edge computing system, providing computing services for ground users that c...
详细信息
Low earth orbit(LEO) satellites with wide coverage can carry mobile edge computing(MEC)servers with computing power to form the LEO satellite edge computing system, providing computing services for ground users that cannot access the core network. This paper studies the joint optimization problem of communication and computing resource in the LEO satellite edge computing system to minimize the utility function value of the system. Due to the fact that, general optimization tools cannot effectively solve this problem, this paper proposes a deep learning-based bandwidth allocation algorithm. The bandwidth allocation schemes are generated through multiple parallel deep neural networks(DNNs).The utility function values of the system are calculated according to the derived optimal CPU cycle frequency and optimal user transmission power. The bandwidth allocation scheme corresponding to the optimal system utility function value is stored in the memory to further train and improve all DNNs. The simulation results show that the proposed algorithm can achieve good convergence effect and the algorithm proposed in this paper outperforms the other four comparison algorithms with low average time cost.
Today's VLSI hardware and IP from third parties are vulnerable to several attacks, thereby companies security. The split manufacturing techniques evolved in market leads to the IP piracy issues. Logic locking is o...
详细信息
There are many problems faced by visually impaired people. Blinds find it very difficult to walk and also do many other small things. Their grief is huge. In this paper a solution is proposed to one of their problems ...
详细信息
Recently, researchers have proposed an emitter localization method based on passive synthetic aperture. However, the unknown residual frequency offset(RFO) between the transmitter and the receiver causes the received ...
详细信息
Recently, researchers have proposed an emitter localization method based on passive synthetic aperture. However, the unknown residual frequency offset(RFO) between the transmitter and the receiver causes the received Doppler signal to shift, which affects the localization accuracy. To solve this issue, this paper proposes a RFO estimation method based on range migration fitting. Due to the high frequency modulation slope of the linear frequency modulation(LFM)-modulation radar signal, it is not affected by RFO in range compression. Therefore, the azimuth time can be estimated by fitting the peak value position of the pulse compression in range ***, the matched filters are designed under different RFOs. When the zero-Doppler time obtained by the matched filters is consistent with the estimated azimuth time, the given RFO is the real RFO between the transceivers. The simulation results show that the estimation error of azimuth distance does not exceed 20 m when the received signal duration is not less than 3 s, the pulse repetition frequency(PRF) of the transmitter radar signal is not less than 1 k Hz, the range detection is not larger than 1 000 km, and the signal noise ratio(SNR) is not less than –5 d B.
Deep learning models enable state-of-the-art accuracy in computer vision applications. However, the deeper, computationally expensive, and densely connected architecture of deep neural networks (DNN) have limitations ...
详细信息
暂无评论