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Table 3 the average F1 score results is visible in the spec-
tral domain. Despite that different paths led us to the archi-
tecture of SkipFCN, the main drive was to achieve compa-
rable performance to time-domain models since input space
was heavily redundant. For keeping our evaluations sym-
metric and providing a reference for evaluating identical ar-
chitectures on the spectral domain, we list results of the
same networks shown in Table 2.
Computational requirements. The delay and complex-
ity of our proposed model, depicted in Figure 1 is mainly
characterized by the Fourier transform, and the complexity
of the two parallel branches, working on time and frequency
domain respectively. In total the model has fewer than 2
million trainable parameters, that takes up 8 MB storage
using 32-bit precision representation. We performed exten-
sive time complexity measurements on the proposed model,
using a single P100 Graphics Processor Unit. For the train-
ing, we used the mini-batch size of 64 with early-stopping
after 100 epochs on the training data. On average, each
training took 1 hour before reaching a peak of accuracy. In
real-time operation (inference) using Fourier Transform win-
dow size of 255, the model requires 354 measurement points
to evaluate the first response. Based on the sampling rate of
the recording device (300 Hz) that translates to a 1.18s ef-
fective delay. Furthermore, we measured how fast the model
evaluates a 20s window. Without computing samples in par-
allel, the time-domain branch takes 1.33 ms ± 821 ns and
the frequency-domain branch takes 1.54 ms ± 1.48 µs to ex-
tract features, in total the inference takes 1.68 ms ± 2.11
µs. Using a batch size of 100 we can parallelize the compu-
tations with a sub-linear increase in time cost: 18.6 ms ±
21.9 µs on time-domain, 32.1 ms ± 127 µs on the frequency
domain, 39.1 ms ± 482 µs when branches are computed in
parallel.
StuCoSReC Proceedings of the 2019 6th Student Computer Science Research Conference 83
Koper, Slovenia, 10 October
tral domain. Despite that different paths led us to the archi-
tecture of SkipFCN, the main drive was to achieve compa-
rable performance to time-domain models since input space
was heavily redundant. For keeping our evaluations sym-
metric and providing a reference for evaluating identical ar-
chitectures on the spectral domain, we list results of the
same networks shown in Table 2.
Computational requirements. The delay and complex-
ity of our proposed model, depicted in Figure 1 is mainly
characterized by the Fourier transform, and the complexity
of the two parallel branches, working on time and frequency
domain respectively. In total the model has fewer than 2
million trainable parameters, that takes up 8 MB storage
using 32-bit precision representation. We performed exten-
sive time complexity measurements on the proposed model,
using a single P100 Graphics Processor Unit. For the train-
ing, we used the mini-batch size of 64 with early-stopping
after 100 epochs on the training data. On average, each
training took 1 hour before reaching a peak of accuracy. In
real-time operation (inference) using Fourier Transform win-
dow size of 255, the model requires 354 measurement points
to evaluate the first response. Based on the sampling rate of
the recording device (300 Hz) that translates to a 1.18s ef-
fective delay. Furthermore, we measured how fast the model
evaluates a 20s window. Without computing samples in par-
allel, the time-domain branch takes 1.33 ms ± 821 ns and
the frequency-domain branch takes 1.54 ms ± 1.48 µs to ex-
tract features, in total the inference takes 1.68 ms ± 2.11
µs. Using a batch size of 100 we can parallelize the compu-
tations with a sub-linear increase in time cost: 18.6 ms ±
21.9 µs on time-domain, 32.1 ms ± 127 µs on the frequency
domain, 39.1 ms ± 482 µs when branches are computed in
parallel.
StuCoSReC Proceedings of the 2019 6th Student Computer Science Research Conference 83
Koper, Slovenia, 10 October
