IEEE Trans Biomed Circuits Syst. 2020 Apr;14(2):373-381. doi: 10.1109/TBCAS.2020.2974049. Epub 2020 Feb 14.
This study aims to design and implement a very large scale integration (VLSI) chip of the extend InfoMax independent component analysis (ICA) algorithm which can separate the super-Gaussian source signals. In order to substantially reduce the circuit area, the proposed circuit utilizes the time sharing matrix multiplication array (MMA) to realize a series of matrix multiplication operations and employs the coordinate rotation digital computer (CORDIC) algorithm to calculate the hyperbolic functions sinh(θ) and cosh(θ) with the rotation of the hyperbolic coordinate system. Also, the rotation of the linear coordinate system of the CORDIC is adopted for the design of a divider used for obtaining the required function value of tanh(θ) simply by evaluating sinh(θ)/cosh(θ). Implemented in a TSMC 90-nm CMOS technology, the proposed ICA has an operation frequency of 100 MHz with 90.8 K gate counts. Furthermore, the measurement results show the ICA core can be successfully applied to separating mixed medical signals into independent sources.
本研究旨在设计和实现一个超大规模集成电路（VLSI）芯片的扩展 InfoMax 独立成分分析（ICA）算法，该算法可以分离超高斯源信号。为了大幅减少电路面积，所提出的电路利用分时矩阵乘法阵列（MMA）来实现一系列矩阵乘法运算，并采用坐标旋转数字计算机（CORDIC）算法通过双曲线坐标系的旋转来计算双曲函数 sinh(θ)和 cosh(θ)。此外，CORDIC 的线性坐标系的旋转也被用于设计一个除法器，通过简单地评估 sinh(θ)/cosh(θ)，即可获得所需的 tanh(θ)函数值。该 ICA 在 TSMC 90nm CMOS 技术下实现，工作频率为 100MHz，门数为 90.8K。此外，测量结果表明，ICA 核心可以成功应用于将混合医疗信号分离为独立源。
