Table 2 The execution of all deep learning methods is timed on the test dataset of 255,701 samples.

From: Scaling up DNA digital data storage by efficiently predicting DNA hybridisation using deep learning

Model

# Params.

Batch

Hardware

Time (s)

Speedup

NUPACK 3

N/A

N/A

64-core VM

372.59

\(\times\)1.00

RoBERTa

6.1M

1024

RTX 3090

388.44 ± 0.32

\(\times\)0.96

RNN

249K

8192

RTX 3090

15.87 ± 0.10

\(\times\)23.47

4096

TPUv2

03.60 ± 0.11

\(\times\)103.50

CNN

2.8M

512

RTX 3090

23.84 ± 0.08

\(\times\)15.63

4096

TPUv2

01.23 ± 0.17

\(\times\) 301.74

\(\text {CNN}_{\text {Lite}}\)

470K

512

RTX 3090

09.01 ± 0.00

\(\times\)41.34

4096

TPUv2

01.28 ± 0.15

\(\times\)290.21

  1. The text in bold corresponds to the best model according to the time/speedup.
  2. The average execution time and the standard deviation are reported in seconds. Each deep learning method is run 10 times, after an initial warm-up run. The time elapsed to load the dataset into memory is not taken into account and the batch size was chosen to maximise inference time. All deep learning models use consumer hardware or openly-available hardware (the TPU platform is completely free to use).
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