Split Architecture for Artificial Intelligence-Based Base Caller

A system, comprising:

• a spatial convolution network configured to process a window of per-cycle sequencing image sets for a series of sequencing cycles of a sequencing run on a cycle-by-cycle basis by separately convolving respective per-cycle sequencing image sets in the window of per-cycle sequencing image sets through respective sequences of spatial convolution layers to generate respective per-cycle spatial feature map sets for respective sequencing cycles in the series of sequencing cycles;
• a temporal convolution network configured to process the per-cycle spatial feature map sets on a groupwise basis by convolving on respective overlapping groups of per-cycle spatial feature map sets in the per-cycle spatial feature map sets using respective temporal convolution filter banks to generate respective per-group temporal feature map sets for the respective overlapping groups of per-cycle spatial feature map sets;
• and
• wherein trained coefficients of temporal convolution filters in the respective temporal convolution filter banks vary between temporal convolution filter banks in the respective temporal convolution filter banks.

An artificial intelligence-based method of base calling, the method including:

• processing, through spatial convolution network, a window of per-cycle sequencing image sets for a series of sequencing cycles of a sequencing run on a cycle-by-cycle basis by separately convolving respective per-cycle sequencing image sets in the window of per-cycle sequencing image sets through respective sequences of spatial convolution layers, and generating respective per-cycle spatial feature map sets for respective sequencing cycles in the series of sequencing cycles;
• wherein the respective sequences of spatial convolution layers have respective sequences of spatial convolution filter banks, wherein trained coefficients of spatial convolution filters in spatial convolution filter banks of the respective sequences of spatial convolution filter banks vary between sequences of spatial convolution layers in the respective sequences of spatial convolution layers;
• processing, through a temporal convolution network, the per-cycle spatial feature map sets on a groupwise basis by convolving on respective overlapping groups of per-cycle spatial feature map sets in the per-cycle spatial feature map sets using respective temporal convolution filter banks of a first temporal convolution layer, and generating respective per-group temporal feature map sets for the respective overlapping groups of per-cycle spatial feature map sets;
• and
• wherein trained coefficients of temporal convolution filters in the respective temporal convolution filter banks vary between temporal convolution filter banks in the respective temporal convolution filter banks.