Generating FPGA accelerators for chemical similarity assessment

Numerical measures of similarity/distance between objects represented by binary vectors are common in a wide range of disciplines. Searching in large-scale chemical databases requires billions of comparisons between molecules that are represented by binary fingerprints to capture the atomic structure. The performance bottleneck here is the enumeration of set bits in vectors (population count). Due to the discrete representation, similarity measures between binary fingerprints should fit well to FPGAs. We present an architecture to accelerate binary similarity assessment, evaluate various design points, and compare performance to highly optimized CPU and GPU implementations. We implement an RTL generation software, SimGenRTL, to generate accelerators of various sizes based on the proposed architecture. We find that accelerators with fewer and wider population counters allow better distribution of the hardware resources, outperforming significantly accelerators with more and narrower bit-enumeration components. SimGenRTL is available for download to allow rapid design space exploration of the computational core ahead of a full custom system implementation.