Analyzing biological data provides critical insights for understanding and treating diseases, personalized medicine, outbreak tracing, evolutionary studies, and agriculture. Modern genome sequencing devices can rapidly generate large amounts of genomic data at a low cost. However, genome analysis is significantly impacted by the computational and data movement overheads of existing computing systems and algorithms, causing significant limitations in terms of speed, accuracy, application scope, and energy efficiency of the analysis.

This talk focuses on designing algorithms and hardware to address these computational limitations in biological data analysis. First, we discuss how to take a fundamentally different approach to genomic data analysis by directly analyzing electrical signals generated by sequencing devices, without converting them into DNA characters. Second, we show how direct analysis of these electrical signals provides us with opportunities to exploit emerging computing paradigms, such as in-memory computing, to perform real-time and energy-efficient analysis directly on edge devices. We conclude by touching on the potential for biological data analysis that can be performed anywhere, anytime, and by anyone to enable fundamentally new applications in medicine and genomics.