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DNNAbacus: Toward Accurate Computational Cost Prediction for Deep Neural Networks
Our experimental results show that the mean relative error (MRE) is 0. 9% with respect to time and 2. 8% with respect to memory for 29 classic models, which is much lower than the state-of-the-art works.
Computational Performance of a Germline Variant Calling Pipeline for Next Generation Sequencing
With the booming of next generation sequencing technology and its implementation in clinical practice and life science research, the need for faster and more efficient data analysis methods becomes pressing in the field of sequencing.
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