Compressive information acquisition is a natural approach for low-power hardware front ends, since most natural signals are sparse in some basis. Key design questions include the impact of hardware impairments (e.g., nonlinearities) and constraints (e.g., spatially localized computations) on the fidelity of information acquisition. Our goal in this paper is to obtain specific insights into such issues through modeling of a Large Area Electronics (LAE)-based image acquisition system. We show that compressive information acquisition is robust to stochastic nonlinearities, and that appropriately designed spatially localized computations are effective, by evaluating the performance of reconstruction and classification based on the information acquired.