Magierowski, SebastianGhafar-Zadeh, EbrahimBeyene, Abel2025-11-112025-11-112023-04-172025-11-11https://hdl.handle.net/10315/43209Nanopore Sequencing is characterized by its high-throughput and long reads which makes it amenable to sequencing genomes in real-time. The vast amounts of data generated, however, imposes serious requirements on any hardware tasked with performing the back-end data processing necessary to convert the electrical signal inputs to base-pair label outputs. The literature contains several examples of machine learning algorithms targeted at nanopore sequencing and demonstrated on server class CPUs and GPUs. Such hardware would be poorly suited to mobile environments however which have tight power and area constraints. To demonstrate the potential of custom accelerators for sequencing tasks, this work presents a complete RISC-V System-on-Chip (SoC) equipped with a bioinformatics hardware accelerator and taped out in Global Foundries 22nm process. It’s able to achieve a 13X speed-up when basecalling over the ARM cortex A53 while only consuming 20 mW power.Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.Electrical engineeringElectronic Thesis or Dissertation2025-11-11DNA sequencingSoCSystem-on-ChipMobile processingApplication specific processorHardware acceleration