Optimizing Self-Organizing Maps for Bacterial Genome Identification on Parallel Ultra-Low-Power Platforms

Pathogenic bacteria significantly threaten human health, highlighting the need for precise and efficient methods for swiftly identifying bacterial species. This paper addresses the challenges associated with performing genomics computations for pathogen identification on embedded systems with limited computational power. We propose an optimized implementation of Self-Organizing Maps (SOMs) targeting a parallel ultra-low-power platform based on the RISC-V instruction set architecture. We propose two mapping methods for implementing the SOM algorithm on a parallel cluster, coupled with software techniques to improve the throughput. Orthogonally to parallelization, we investigate the impact of smaller-than-32-bit floating-point formats (smallFloats) on energy savings, precision, and performance. Our experimental results show that all smallFloat formats exhibit a 100% classification accuracy. The parallel variants achieve a speed-up of $1.98 \times , 3.79 \times$, and $6.83 \times$ on 2, 4, and 8 cores, respectively. Comparing our design with a 16-bit fixed-point implementation on a coarse grain reconfigurable architecture (CGRA), the FP8 implementation achieves, on average, $1 . 42 \times$ energy efficiency, $1 . 51 \times$ speedup, and a 50% reduction in memory footprint compared to CGRA. Furthermore, FP8 vectorization increases the average speed-up by $2.5 \times$.