Configurable Independent Component Analysis Preprocessing Accelerator

Hsi-Hung Lu, Chung An Shen, Mohamed E. Fouda, Ahmed Eltawil

Research output: Contribution to journalArticlepeer-review

Abstract

An independent component analysis (ICA) has been used in many applications, including self-interference cancellation (SIC) for in-band full-duplex (IBFD) wireless systems and anomaly detection in industrial Internet of Things (IoT). This article 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 the EVD are conducted simultaneously and are individually pipelined to maximize throughput. This article 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/s at a complexity of 73.3 kGE.
Original languageEnglish (US)
Pages (from-to)1-13
Number of pages13
JournalIEEE Transactions on Very Large Scale Integration (VLSI) Systems
DOIs
StatePublished - Oct 5 2022

ASJC Scopus subject areas

  • Hardware and Architecture
  • Software
  • Electrical and Electronic Engineering

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