  Independent component analysis (ICA) has been used in many applications,
including self-interference cancellation for in-band full-duplex wireless
systems and anomaly detection in industrial internet of things. This paper
presents a high-throughput and highly efficient configurable preprocessing
accelerator for the ICA algorithm. The proposed ICA accelerator has three major
blocks that perform data centering, covariance matrix for computation, and
eigenvalue decomposition (EVD). Specifically, the proposed accelerator is based
on a high-performance matrix multiplication array (MMA). The proposed MMA
architecture uses time-multiplexed processing so that the efficiency of
hardware utilization is greatly enhanced. Furthermore, the processing flow
utilizes parallel processing such that the centering, the calculation of the
covariance matrix, and EVD are conducted simultaneously and are individually
pipelined to maximize throughput. This paper presents the architecture, circuit
design, and performance estimates based on post-layout extraction of the
proposed preprocessing ICA accelerator. The proposed design achieves a
throughput of 40.7 kMatrices per second at complexity of 73.3 kGE.
