VLSI delta-sigma cellular neural network for analog random vector generation

We present a cellular neural network architecture for parallel analog random vector generation, including experimental results from an analog VLSI prototype with 64 channels. Nearest-neighbor coupling between cells produces parallel channels of uniformly distributed random analog values, with statistics that are truly uncorrelated across channels and over time. The cell for each random channel emulates an integrating nonlinearity essentially implementing a delta-sigma modulator, and measures 100 mu m x 120 mu m in 2 mu m CMOS technology. Applications include analog encryption and secure communications, analog built-in self-test, stochastic neural networks, and simulated annealing optimization and learning.