The Problem
GeneWeaver started as an exploration into hardware acceleration for bioinformatics. I was working with DNA sequence alignment algorithms and noticed that software implementations were hitting performance bottlenecks with larger datasets. The idea was to create a parametric hardware generator that could produce optimized FPGA or ASIC designs tailored to specific alignment algorithms.
The Approach
The core challenge was building a system flexible enough to handle different sequence lengths and alignment patterns while still generating efficient hardware. I chose Scala for this because its strong type system and functional programming features made it easier to reason about hardware generation logic. The generator takes configuration parameters and produces hardware descriptions that can be synthesized for different target platforms.
Why It Works
What makes this interesting is how it adapts to different bioinformatics workloads. Rather than building a one-size-fits-all solution, the parametric approach lets researchers tune the hardware for their specific use cases. This has proven useful for genomics research where processing speed can be a real constraint.
Implementation
The implementation focuses on creating modular, extensible designs. As new alignment algorithms emerge or requirements change, the generator can be extended without rewriting the core infrastructure. It's been a solid learning experience in both hardware design principles and functional programming applied to domain-specific problems.