Approximate Computing Applied to Bacterial Genome Identification using Self-Organizing Maps

In this paper we explore the design space of a self-organizing map (SOM) used for rapid and accurate identification of bacterial genomes. This is an important health care problem because even in Europe, 70% of prescriptions for antibiotics is wrong. The SOM is trained on Next Generation Sequencing (NGS) data and is able to identify the exact strain of bacteria. This is in contrast to conventional methods that require genome assembly to identify the bacterial strain. SOM has been implemented as an synchoros VLSI design and shown to have 3-4 orders better computational efficiency compared to GPUs. To further lower the energy consumption, we exploit the robustness of SOM by successively lowering the resolution to gain further improvements in efficiency and lower the implementation cost without substantially sacrificing the accuracy. We do an in depth analysis of the reduction in resolution vs. loss in accuracy as the basis for designing a system with the lowest cost and acceptable accuracy using NGS data from samples containing multiple bacteria from the labs of one of the co-authors. The objective of this method is to design a bacterial recognition system for battery operated clinical use where the area, power and performance are of critical importance. We demonstrate that with 39% loss in accuracy in 12 hits and 1% in 16 bit representation can yield significant savings in energy and area.