Abstract: Accelerating matrix multiplication is crucial to achieve high performance in many application domains, including neural networks, graph analytics, and scientific computing. These ...
Abstract: Numerous studies have proposed hardware architectures to accelerate sparse matrix multiplication, but these approaches often incur substantial area and power overhead, significantly ...
This is a benchmarking tool for Qdrant's sparse vector implementation using the NeurIPS 2023 datasets. This task is based on the common MSMARCO passage retrieval dataset, which has 8,841,823 text ...
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