A Hardware Efficient Matrix Multiplications Scheme with Dynamic Precisions and Dimensions for Massive MIMO Systems

Matrix multiplication serves as the primary operation in massive multiple-input multiple-output (MIMO). However, with the continuous advancement of MIMO technology, the escalating computational complexity and the necessity for adaptable matrix multiplication in MIMO communication pose a formidable challenge. In this paper, we propose the Joint Serial-Parallel Dataflow (JSPD) mapping method for dynamic precision matrix multiplication, which relies on the utilization of Spatio-Temporal Transforms (STT) and Principal-Auxiliary Matrices (PAM). The data is initially processed serially and is mapped onto Processing Element (PE) arrays through STT transforms. Subsequently, both high-precision and low-precision components of the data are computed selectively in parallel, employing a combination of principal and auxiliary matrices. The matrix multiplication process is mapped onto the PE arrays, incorporating output-stationary and output-flow modes. Compared with the traditional fixed structure PE arrays, the JSPD mapping method improves the computational flexibility while increasing the PE cell work share ratio by 7.57% ∼ 21.86%. Meanwhile, JSPD reduces the hardware overhead by a maximum of 68% and improves hardware efficiency by 35.77%.