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Inferring the properties of black holes and neutron stars is a key sciencegoal of gravitational-wave (GW) astronomy. To extract as much information aspossible from GW observations we must develop methods to reduce the cost ofBayesian inference. In this paper, we use artificial neural networks (ANNs) andthe parallelisation power of graphics processing units (GPUs) to improve thesurrogate modelling method, which can produce accelerated versions of existingmodels. As a first application of our method, ANN-Sur, we build a time-domainsurrogate model of the spin-aligned binary black hole (BBH) waveform modelSEOBNRv4. We achieve median mismatches of 2e-5 and mismatches no worse than2e-3. For a typical BBH waveform generated from 12 Hz with a total mass of $60M_\odot$ the original SEOBNRv4 model takes 1812 ms. Existing bespoke codeoptimisations (SEOBNRv4opt) reduced this to 91.6 ms and the interpolationbased, frequency-domain surrogate SEOBNRv4ROM can generate this waveform in 6.9ms. Our ANN-Sur model, when run on a CPU takes 2.7 ms and just 0.4 ms when runon a GPU. ANN-Sur can also generate large batches of waveforms simultaneously.We find that batches of up to 10^4 waveforms can be evaluated on a GPU in just163 ms, corresponding to a time per waveform of 0.016 ms. This method is apromising way to utilise the parallelisation power of GPUs to drasticallyincrease the computational efficiency of Bayesian parameter estimation.

Gravitational-wave surrogate models powered by artificial neural networks: The ANN-Sur for waveform generation