A VLSI Neural Network Processor Based on Hippocampal Model

The hippocampal region of the brain system can be analyzed with the nonlinear system modeling approach. The input-output relationship of the neural units is best represented by the kernel functions of different complexities. The modeling expression of the first and second order kernels are computed in analog current-mode instead of digital data processing in order to fully explore massively parallel processing capability of the neural networks. Two distinct methods are utilized: the table-look-up approach and the model-based approach. The former can achieve high accuracy but consumes large silicon area while the latter saves silicon area and maintains moderately high accuracy. Circuit-level simulation results and experimental data from two test structures are presented.