EMO-CIM: An Input/Stationary-Data Similarity-Aware Computing-In-Memory Design for Variable Vector-Wise Computation in Edge Multioperator AI Acceleration https://sscs.ieee.org/wp-content/themes/movedo/images/empty/thumbnail.jpg 150 150 https://secure.gravatar.com/avatar/8fcdccb598784519a6037b6f80b02dee03caa773fc8d223c13bfce179d70f915?s=96&d=mm&r=g
Abstract:
We propose an edge multioperator computing-in-memory (EMO-CIM) design that supports variable vector-wise multiply-and-accumulate (MAC) in CNN, Depthwise (DW)-Convolution, and Attention operators. It features: 1) a single EMO-CIM bank (ECB) excels in variable vector-wise MAC (V-MAC) for multioperators; 2) merging local input-shared compute units (LISCUs) with a decode-unit and adder-tree (DUAT) facilitates …