Week 1: Introduction

Hi everyone. My name is Charan Sridhar, and over the next few months, I’ll be working on a machine learning project focused on nanopore sequencing. I’ll use this blog to document what I’m doing, what I’m learning, and the problems I run into along the way.

Here’s the context. Nanopores are tiny pores that can detect molecules by measuring changes in electrical current as those molecules pass through. The technology was originally developed for DNA sequencing, but researchers have started applying it to other areas like water quality testing. Last summer, I worked with Dr. Cherukumilli at the University of Washington’s SWELL Lab on a project that used nanopores to detect contaminants in water. While I was there, I noticed that the software used to interpret the nanopore signal, called a basecaller, requires a lot of computational power. These models are built to run on high-end GPUs in data centers. That’s a problem if you want to use a nanopore sensor in the field, in a place without reliable internet or powerful hardware.

That’s the problem I’m trying to address. My research question is: how to compress a basecalling neural network without losing meaningful accuracy, so that it can run on low-power devices that can be deployed in the field? I’ll be looking at techniques like model pruning, quantization, and knowledge distillation. Most of the work on basecaller optimizations is about making basecallers faster on high-end GPUs, not on small CPU edge devices.

My hypothesis is that using techniques like quantization and pruning, I will be able to build a compressed model that is accurate enough to be practically useful in the field for certain applications like water quality testing, even if it doesn’t match the performance of a production-level basecaller like Dorado.