Accelerating sequence alignment using in-memory processing architectures

Description

This project focuses on the design of In-Memory Processing (IMP) accelerators for computational biology workloads. Their work explores how memory-limited algorithms, such as sequence alignment and pangenome analysis, can be optimized by performing the computation directly in memory, thereby reducing costly data transfers. By combining innovative algorithms with cutting-edge microelectronics, their project contributes to building scalable and energy-efficient solutions for large-scale genomic pipelines, directly supporting biomedical research and healthcare applications.

Background

Alejandro obtained his degree in Computer Engineering from the Universitat Autònoma de Barcelona (UAB) in 2019. He subsequently worked for two years at the ALBA Synchrotron in Barcelona, gaining practical experience in computational and engineering environments. In 2023, he completed a master's degree in High Performance Computing from the Universitat Politècnica de Catalunya (UPC). He is currently pursuing his PhD in the Department of Computer Science at BSC, where his research connects algorithm design and hardware acceleration for bioinformatics, with a focus on In-Memory Processing (IMP).

Motivation

Alejandro is motivated by the urgent need for efficient computational methods in bioinformatics and biomedicine, where massive data sets increasingly challenge conventional systems. His research investigates algorithms and hardware accelerators that exploit In-Memory Processing (IMP) to reduce data movement, improve performance and enable near-real-time genomic analysis. The support of the Chip Chair is key to validating and scaling these approaches, as it connects innovation in microelectronics with the needs of computational biology. Leveraging this ecosystem, Alejandro aspires to contribute to Europe's leadership in advanced hardware, while delivering impactful applications in healthcare and personalized medicine. Alejandro's research interests focus on In-Memory Processing architectures, hardware acceleration for bioinformatics, sequence alignment, and data movement reduction in large-scale genomic analysis.