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The efficiency of these accelerators comes from employing optimized dataflow (i.e., spatial/temporal partitioning of data across the PEs and fine-grained scheduling) strategies to optimize data reuse.
Approximated Matrix Multiplication (AMM) based on table look-ups can significantly reduce the pressure on computing units and memory bandwidth, and has great potential in large-scale machine learning ...