“…Using SNN enables the proposed solution to be easily realized on hardware using FPGA, without requiring CPU or additional memory. In addition, we also discuss techniques for optimal SNN-based implementation using efficient number of neurons for the required accuracy and resolution along each dimension, without increasing the overall computational complexity. …”
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“…BindsNET is built on the PyTorch deep neural networks library, facilitating the implementation of spiking neural networks on fast CPU and GPU computational platforms. Moreover, the BindsNET framework can be adjusted to utilize other existing computing and hardware backends; e.g., TensorFlow and SpiNNaker. …”
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“…However, when applied to large genomic data sets, these tools require high memory and CPU usage, typically available only in computational clusters. …”
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“…Besides, our system runs independent of Graphic Processing Unit (GPU) and reaches 40 frames per second (FPS) at about 25% Central Processing Unit (CPU) usage.…”
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“…Our deep learning approach was 120-folder faster than the traditional reconstruction method and completed the analysis in < 2 min using a single CPU. In a blinded clinical study using patient cervical brushings, we successfully benchmarked our platform's performance to an FDA-approved HPV assay. …”
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“…Experimental results on benchmark circuits show that the proposed SAT-based methodology accurately determines the maximum error rate and an average-case error within acceptable CPU execution time in one go, and further provides a log of error-generating input assignments.…”
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“…Moreover, the use of this predictor has a low cost in terms of CPU and memory consumption, and it can also be used with other routing protocols.…”
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“…In addition, we present the computational convergence and error studies for LPs resulting convergent polynomials and error tendency to zero as the order of LPs increases for all study cases. Finally, a study of CPU time shows that LPs require a few nano-seconds to be evaluated, which makes them suitable for intensive computing applications.…”
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“…We implemented the technique in magnetic tweezers and tracked the 3D position of over 100 beads in real time on a generic CPU. The accuracy of 3D phasor tracking was extensively tested and compared to a look-up table approach using Lorenz-Mie simulations, avoiding experimental uncertainties. …”
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“…The experimental results showed that the resulting matching time was around 32× faster than that achieved on a conventional central processing unit (CPU) for a test image size of 1280 × 960 pixels. …”
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“…We demonstrate for the ExaHyPE engine—a task-based solver for hyperbolic equation systems—that it is possible to realise resiliency without major code changes on the user side, while we introduce a novel algorithmic idea where replication reduces the time-to-solution. The redundant CPU cycles are not burned “for nothing”. Our work employs a weakly consistent data model where replicas run independently yet inform each other through heartbeat messages whether they are still up and running. …”
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“…In the experiment, we deploy this framework on a desktop computer with a 4-core Intel CPU. It took only a few minutes to extract the power spectrum features from the 2.85 GB EEG dataset, seven times faster than using Python. …”
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“…Polylidar3D is shown to be quite fast, making use of CPU multi-threading and GPU acceleration when available. …”
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“…METHODS: Cerebral hemorrhage model was successfully achieved by injecting collagenase VII (causing stabile localized bleeding) in CPu (striatum) of SD rats. Emodin was used as a potential therapeutic for ICH. …”
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“…As a result, our method always had the highest average statistical power compared with multi-locus mixed-model (MLMM), fixed and random model circulating probability unification (FarmCPU), and genome-wide efficient mixed model association (GEMMA). …”
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“…Specifically, ENet obtains a dice similarity coefficient of 90.89% and a segmentation time of about 6 s using central processing unit (CPU) hardware to simulate real clinical conditions where graphics processing unit (GPU) is not always available. …”
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“…It was performance tested on images from 2 MB (161 particles) to 6.5 GB (437 508 particles) to determine whether theoretical linear scaling (O(n)) had been achieved, and on 1–40 CPU threads to measure speed improvements due to multithreading. …”
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“…A genome-wide association study (GWAS) was performed on a resource family consisting of white and colored chickens for identification of genes related to plumage coloration using the Fixed and random model Circulating Probability Unification (FarmCPU) package. GWAS identified three chromosomal single-nucleotide polymorphisms (SNPs), demonstrating the polygenic basis of plumage phenotypes. …”
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“…The High Energy Physics (HEP) experiments, such as those at the Large Hadron Collider (LHC), traditionally consume large amounts of CPU cycles for detector simulations and data analysis, but rarely use compute accelerators such as GPUs. …”
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“…These services have been further developed in subsequent initiatives under H2020 projects and are now operating as Thematic Services in the European Open Science Cloud portal (www.eosc-portal.eu), sending >12 millions of jobs and using around 4,000 CPU-years per year. Here we review 10 years of successful e-infrastructure solutions serving a large worldwide community of over 23,000 users to date, providing them with user-friendly, web-based solutions that run complex workflows in structural biology. …”
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