부산대학교 도서관

Compute-in-memory accelerators have been extensively researched to overcome the limitations of the von Neumann architecture. However, the current mapping strategy and dataflow results in inefficient utilization of the array and input data. In this brief, we propose a new mapping method named Squeezemapping that leverages spare space in each array and optimizes the utilization of input dataset. We employed NeuroSim to simulate the inference of various networks of different scales. Experimental results demonstrate that our method performs 36.51% higher in energy efficiency and 48.15% higher in speedup when applied to the VGG16 large-scale model under area constraints.