A power-efficient and re-configurable analog artificial neural network classifier. (May 2021) - Mohamed, Ahmed Reda; Qi, Liang; Wang, Guoxing
Abstract: This paper presents a power-efficient and re-configurable radial basis function-artificial neural network (RBF-ANN) for real-time pattern classification tasks. We have developed a MATLAB-based behavioral model, which can be employed to facilitate the off-chip learning of the proposed network with a hybrid learning algorithm. Besides, a generic design methodology is presented to ease implementing different scales of neural networks. For the RBF-ANN, an analog tunable Gaussian kernel circuit is used as an activation neuron while a current-mode computation is employed to improve the power efficiency. The proposed network is designed and simulated in 0.18  μ m X-FAB CMOS process for a non-linear XOR-pattern classification and voice activity detector (VAD), respectively. It is found that the network's weights obtained using the developed model in MATLAB are well-matched with the Spectre simulation (Virtuoso) results with a maximum relative error of 7.1%. Consequently, the proposed model can be effectively employed for the off-chip training of RBF-ANN. Besides, the preliminary simulation results of the VAD depict that the classification accuracy reaches as large as 95% in a noisy environment of babble with an acoustic signal to noise ratio (SNR) of 10 dB. Meanwhile, the power efficiency of the proposed network gets significantly improved compared to the recent prior arts.