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It is commonly accepted that ‘the brain computes’ and that it serves as a model for establishing principles of technical (first of all, electronic) computing. Even today, some biological implementation details inspire the implementation of more performant electronic implementations. However, grasping details without context often leads to decreasing operating efficiency. In the cases of those major implementations, the notion of ‘computing’ has an entirely different meaning. We provide the notion of generalized computing from which we derive technical and biological computing, and by showing how the functionalities are implemented, we also highlight what performance losses lead the solution. Both implementations have been developed using a success–failure method, keeping the successful part-solutions (and building on top of them) and replacing a less successful one with another. Both developments proceed from a local minimum of their goal functions to another, but some principles differ fundamentally. Moreover, they apply entirely different principles, and the part-solutions must cooperate with others, so grasping some biological solution without understanding its context and implementing it in the technical solution usually leads to a loss of efficiency. Today, technical systems’ absolute performance seems to be saturated, while their computing and energetic inefficiency are growing.

Source-independent Q-compensated least-squares reverse time migration based on the convolutional misfit function constitutes an amplitude-preserving methodology that effectively compensates for seismic attenuation and alleviates the constraints imposed by the source wavelet. Nevertheless, its conventional implementation, which constructs the gradient through the cross-correlation between background wavefield and adjoint wavefield propagating in opposite directions, incurs exorbitant storage and computational expenses. To ease the computational and storage pressures, we developed an efficient source-independent Q-compensated scheme by introducing a local Nyquist cross-correlation imaging condition to formulate gradient. Instead of employing entire wavefields for migration, the local Nyquist cross-correlation imaging condition, in combination with the Nyquist rate, adopts only the local wavefields around the excitation amplitude time. Consequently, the proposed scheme considerably diminishes the storage requirement as well as the additional time resulting from frequent input–output operations, thereby enhancing computational efficiency. Numerical examples conducted on the 2D layered model, Marmousi model, field data, and the 3D Overthrust model reveal that the proposed scheme is capable of attaining imaging accuracy comparable to that of the conventional scheme, while exhibiting superior storage and computing performance, and possesses higher feasibility in 3D applications.

In this work, we investigate device-to-device (D2D) mobile edge computing (MEC) with intelligent reflecting surface (IRS) aiding both D2D and device-to-MEC links to reduce energy and delay for user devices. We formulate a non-convex computation offloading problem that maximizes computing efficiency over latency under mixed integer, linear, and non-linear constraints and jointly optimize offloading decisions, partial offloading ratio, edge computing frequency, transmit power, and IRS phase shifts via a fully connected deep neural network (DNN) trained with a Lagrange-dual loss function that embeds the objective and inequality constraints. Extensive simulations across varying maximum task sizes (0.5-3 Mbits), edge computing capacities (10-90 Mcycles/s), and number of IRS elements (10-30) show that the proposed method consistently achieves solutions within 5-10% of near-global optimal solutions obtained by exhaustive search (ES) and outperforms gradient search (GS), fixed-offloading DNN and REINFORCE algorithm that achieves approximately 50% higher computing efficiency at N 1 = 20 users. The proposed DNN exhibits 3-5% energy consumption gap from ES while maintaining lower computational complexity of O ( 12 N c N 2 M s ) versus O ( ω - 2 ) for GS, O ( ( 2 Q s ) ( N + M ) ) for ES, and O ( ι - 4 ) for REINFORCE, which exhibits inference times on the order of milliseconds, compared to the computationally intractable costs associated with ES. These results show that the proposed algorithm for IRS-assisted partial D2D/MEC offloading is an effective and practical approach for jointly enhancing computing efficiency and latency in next-generation wireless systems.

Unmanned aerial vehicles (UAVs) have emerged as a promising platform for supporting integrated sensing, computing, and communication (ISCC) functionality in Internet of Things (IoT) applications. This paper investigates a UAV-enabled ISCC network, where the UAV performs radar sensing and onboard edge computing with the computational assistance of ground access points (APs). Given the limited onboard energy, ensuring energy-efficient operation of UAVs is crucial to support the long-term sustainability of network performance. In this paper, we define computing efficiency as the ratio between the total number of successfully processed computational bits and the overall UAV energy consumption, under the constraint of a required sensing threshold. To maximize this performance metric, this paper jointly optimizes the beamforming vector, the CPU frequency, and the trajectory of the UAV. This optimization problem is modeled as a Markov decision process (MDP) and solved using a deep reinforcement learning (DRL) approach based on a memory mechanism. Specifically, a long short-term memory (LSTM) and twin delayed deep deterministic policy gradient (TD3)-based trajectory design and resource allocation (LTTDRA) algorithm is proposed. LSTM units are integrated into the actor and critic to effectively capture the temporal correlations in dynamic environments, thereby enhancing policy stability and accelerating algorithm convergence. The reward function is meticulously designed to alleviate sparse-penalty effects and learn high-performance strategies in complex environments with multiple constraints. Extensive simulations are conducted under various settings and network scenarios, and the results consistently indicate that the proposed approach substantially outperforms the baseline schemes.

Experience shows that cooperating and communicating computing systems, comprising segregated single processors, have severe performance limitations, which cannot be explained using von Neumann’s classic computing paradigm. In his classic “First Draft,” he warned that using a “too fast processor” vitiates his simple “procedure” (but not his computing model!); furthermore, that using the classic computing paradigm for imitating neuronal operations is unsound . Amdahl added that large machines, comprising many processors, have an inherent disadvantage. Given that artificial neural network’s (ANN’s) components are heavily communicating with each other, they are built from a large number of components designed/fabricated for use in conventional computing, furthermore they attempt to mimic biological operation using improper technological solutions, and their achievable payload computing performance is conceptually modest. The type of workload that artificial intelligence-based systems generate leads to an exceptionally low payload computational performance, and their design/technology limits their size to just above the “toy” level systems: The scaling of processor-based ANN systems is strongly nonlinear. Given the proliferation and growing size of ANN systems, we suggest ideas to estimate in advance the efficiency of the device or application. The wealth of ANN implementations and the proprietary technical data do not enable more. Through analyzing published measurements, we provide evidence that the role of data transfer time drastically influences both ANNs performance and feasibility. It is discussed how some major theoretical limiting factors, ANN’s layer structure and their methods of technical implementation of communication affect their efficiency. The paper starts from von Neumann’s original model, without neglecting the transfer time apart from processing time, and derives an appropriate interpretation and handling for Amdahl’s law. It shows that, in that interpretation, Amdahl’s law correctly describes ANNs.

The integration of blockchain and edge computing presents a transformative potential to enhance security, computing efficiency, and data privacy across diverse industries. This paper begins with an overview of blockchain and edge computing, establishing the foundational technologies for this synergy. It explores the key benefits of their integration, such as improved data security through blockchain’s decentralized nature and reduced latency via edge computing's localized data processing. Methodologically, the paper employs a systematic analysis of existing technologies and challenges, emphasizing issues such as scalability, managing decentralized networks, and ensuring independence from cloud infrastructure. A detailed Ethereum-based case study demonstrates the feasibility and practical implications of deploying blockchain in edge computing environments, supported by a comparative analysis and an algorithmic approach to integration. The conclusion synthesizes the findings, addressing unresolved challenges and proposing future research directions to optimize performance and ensure the seamless convergence of these technologies.

In applying a hierarchical mixed model to genome-wide association analysis (GWAS) of longitudinal data, dimensionality reduction through modeling repeated measurements improves both computational efficiency and statistical power. Legendre polynomials can flexibly fit population growth trajectories, but higher orders substantially increase computational complexity. Instead of using Legendre polynomials, we first estimated fewer individual-specific parameters using biologically meaningful non-linear models and then associated these phenotypic regressions with genetic markers using a multivariate linear mixed model (mvLMM). After performing a canonical transformation of the regressions based on the pre-estimated covariance matrices under the null genomic mvLMM, we decomposed the mvLMM into mutually independent univariate models and incorporated EMMAX to enable rapid genome-wide mixed-model associations for each transformed phenotype. Simulations for longitudinal association analysis in maize and GWAS for the growth trajectories of body weights in mice demonstrated the advantages of hierarchical non-linear mixed models in computing efficiency and statistical power for detecting quantitative trait loci (QTL), compared with mvLMM for multiple growth points and the hierarchical random regression model using Legendre polynomials as sub-models.

The response capacities of urban flood forecasting and risk control can be improved by strengthening the computational abilities of urban flood numerical models. In this work, a GPU-based hydrodynamic model is developed to simulate urban rainstorm inundations. By simulating rainstorm floods in a certain area of Xixian New City, the established model can implement high-resolution urban rainstorm inundation simulations with significantly accelerated computing performances. The accelerated computation efficiencies of the different rainstorm event simulations under resolutions of 5 and 2 m are quantitatively analysed, showing that the absolute and relative speedup ratios for all scenarios of applying two GPUs range from 10.8 to 12.6 and 1.32 to 1.68 times as much as those of a CPU and a single GPU, respectively. The application of a large-scale rainstorm inundation simulation shows the excellent acceleration performance of the model compared to previous research. In addition, the greater the number of computational grids included in the simulation, the more significant the effect on the acceleration computing performance. The proposed model efficiently predicts the spatial variation in the inundation water depth. The simulation results provide guidance for urban rainstorm inundation management, and they improve the time and efficiency of urban flood emergency decision-making.

Today’s computing is based on the classic paradigm proposed by John von Neumann, three-quarters of a century ago. That paradigm, however, was justified for (the timing relations of) vacuum tubes only. The technological development invalidated the classic paradigm (but not the model!). It led to catastrophic performance losses in computing systems, from the operating gate level to large networks, including the neuromorphic ones. The model is perfect, but the paradigm is applied outside of its range of validity. The classic paradigm is completed here by providing the “procedure” missing from the “First Draft” that enables computing science to work with cases where the transfer time is not negligible apart from the processing time. The paper reviews whether we can describe the implemented computing processes by using the accurate interpretation of the computing model, and whether we can explain the issues experienced in different fields of today’s computing by omitting the wrong omissions. Furthermore, it discusses some of the consequences of improper technological implementations, from shared media to parallelized operation, suggesting ideas on how computing performance could be improved to meet the growing societal demands.

3D-HEVC is the state-of-the-art video coding standard for 3D video, and it is an extension of high efficiency video coding (HEVC) standard. Besides the original HEVC coding tools, 3D-HEVC adopts some advanced coding tools, such as disparity vector (DV), inter-view prediction and inter-component prediction. However, these advanced tools lead to extremely high encoding complexity at the same time, thus it cannot be well applied in real-time multimedia systems. In this paper, we propose a motion and disparity vectors early determination algorithm to reduce 3D-HEVC computational complexity. First, based on the statistical analyses, the spatial and temporal motion vector (MV) candidates are adaptively reduced for the prediction unit (PU) with the Merge mode. Then, for the PU with the Inter mode, the combination of spatial and temporal candidates is used to early determine the final MV. Finally, an adaptive optimization algorithm is adopted to select the valid inter-view disparity vectors (DV) candidates. Moreover, if the difference between candidate vectors is within a conditional range, current PU will be encoded with the Merge mode to skip unnecessary coding process. Experimental results show that for the texture views encoding, the proposed algorithm achieves an average of 33.03% encoding time saving, and an average of 0.47% BD-Rate increases.