HDNA: Energy-Efficient DNA Sequencing using Hyperdimensional Computing

Abstract

DNA sequencing has a vast number of applications in a multitude of applied fields including, but not limited to, medical diagnosis and biotechnology. In this paper, we propose HDNA to apply the concepts of hyperdimensional (HD) computing (computing with hypervectors) to DNA sequencing. HDNA first assigns holographic and (pseudo)random hypervectors to DNA bases. Using an encoder, it then exploits the orthogonality of these hypervectors to represent a DNA sequence by generating a class hypervector. The class hypervector keeps the information of combined individual hypervectors (i.e., the DNA bases) with high probability. HDNA uses the same encoding to map a DNA sequence with unknown labels to a query hypervectors and performs the classification task by checking the similarity of the query hypervector against all class hypervectors. Our experimental evaluation shows that HDNA can achieve 99.7% classification accuracy for Empirical dataset which is 5.2% higher than state-of-the-art techniques for the same dataset. Moreover, our HDNA can improve the execution time and energy consumption of classification by 4.32× and 2.05× respectively, when compared against prior techniques