This research project addresses the need for computationally efficient basecalling models to enable nanopore sequencing on edge devices. Nanopores work by measuring the electrical signal change as molecules pass through tiny conductive holes. They are being explored as a cost-effective and accurate method for water quality monitoring in low-resource environments. However, current basecalling models require high-power GPUs to convert raw signals into identifiable sequences. This project investigates how to develop an accurate basecalling neural network that can operate on edge devices with limited processing power, avoiding the need for powerful devices or internet access to cloud servers.

By exploring optimization techniques like model pruning, low-bit quantization, and knowledge distillation, I aim to build accurate models that meet the hardware constraints of field-deployable systems. My methodology involves an iterative development cycle of building and testing models to maximize accuracy while minimizing their computational complexity. The final product will be an open-source, executable machine learning model capable of sequencing on devices with limited computational power.