  Recent efforts to improve the performance of neural network (NN) accelerators
that meet today's application requirements have given rise to a new trend of
logic-based NN inference relying on fixed-function combinational logic (FFCL).
This paper presents an innovative optimization methodology for compiling and
mapping NNs utilizing FFCL into a logic processor. The presented method maps
FFCL blocks to a set of Boolean functions where Boolean operations in each
function are mapped to high-performance, low-latency, parallelized processing
elements. Graph partitioning and scheduling algorithms are presented to handle
FFCL blocks that cannot straightforwardly fit the logic processor. Our
experimental evaluations across several datasets and NNs demonstrate the
superior performance of our framework in terms of the inference throughput
compared to prior art NN accelerators. We achieve 25x higher throughput
compared with the XNOR-based accelerator for VGG16 model that can be amplified
5x deploying the graph partitioning and merging algorithms.
