RiBoSOM : Rapid Bacterial Genome Identification Using Self-Organizing Map implemented on the Synchoros SiLago Platform

Artificial Neural Networks have been applied to many traditional machine learning applications in image and speech processing. More recently, ANNs have caught attention of the bioinformatics community for their ability to not only speed up by not having to assemble genomes but also work with imperfect data set with duplications. ANNs for bioinformatics also have the added attraction of better scaling for massive parallelism compared to traditional bioinformatics algorithms. In this paper, we have adapted Self-organizing Maps for rapid identification of bacterial genomes called BioSOM. BioSOM has been implemented on a design of two coarse grain reconfigurable fabrics customized for dense linear algebra and streaming scratchpad memory respectively. These fabrics are implemented in a novel synchoros VLSI design style that enables composition by abutment. The synchoricity empowers rapid and accurate synthesis from Matlab models to create near ASIC like efficient solution. This platform, called SiLago (Silicon Lego) is benchmarked against a GPU implementation. The SiLago mentation of BioSOMs in four different dimensions, 128, 256, 512 and 1024 Neurons, were trained for two E Coli strains of bacteria with 40K training vectors. The results of SiLago implementation were benchmarked against a GPU GTX 1070 implementation in the CUDA framework. The comparison reveals 4 to 140x speed up and 4 to 5 orders of improvement in energy-delay product compared to implementation on GPU. This extreme efficiency comes with the added benefit of automated generation of GDSII level design from Matlab by using the Synchoros VLSI design style.