Comparative Study of VLSI Solutions to Independent Component Analysis

The advent of independent component analysis (ICA) has brought a paradigm shift to signal and image processing. ICA that extracts independent source signals by searching for a linear or nonlinear transformation and minimizing the statistical dependence between components has the promise of effective unsupervised signal separation capability. Due to the computation complexity of ICA and commonly high-volume data sets used in signal and image processing, the ICA process, however, is very time-consuming. Very large scale integration (VLSI) solutions with optimal parallelism provide potentially faster and even real-time implementations for ICA algorithms. In this paper, the authors study these solutions and discuss their limits. Critical challenges are identified, and issues associated with the VLSI implementation of ICA algorithms are designed. Design recommendations that have potentials in performing complicated ICA algorithms on large throughput are provided