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Top-K SpMV is a key component of similarity-search on sparse embeddings. Thissparse workload does not perform well on general-purpose NUMA systems thatemploy traditional caching strategies. Instead, modern FPGA accelerator cardshave a few tricks up their sleeve. We introduce a Top-K SpMV FPGA design thatleverages reduced precision and a novel packet-wise CSR matrix compression,enabling custom data layouts and delivering bandwidth efficiency oftenunreachable even in architectures with higher peak bandwidth. With HBM-basedboards, we are 100x faster than a multi-threaded CPU implementation and 2xfaster than a GPU with 20% higher bandwidth, with 14.2x higherpower-efficiency.

Scaling up HBM Efficiency of Top-K SpMV for Approximate Embedding Similarity on FPGAs