AI RESEARCH
RSR-core: A High-Performance Engine for Low-Bit Matrix-Vector Multiplication
arXiv CS.LG
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ArXi:2603.27462v1 Announce Type: cross Matrix-vector multiplication is a fundamental building block in neural networks, vector databases, and large language models, particularly during inference. As a result, efficient matrix-vector multiplication engines directly translate into efficient inference. Recent work has explored low-bit quantization of model weights, where matrices are represented using binary (1-bit) or ternary (1.58-bit) values while activation is kept in higher precision. These representations enable efficient hardware-level computation.