This work presents a framework for behavioral simulations of smart imagers with hardware and power constraints. The objective is to compare innovative imaging systems that would be composed of a specific image sensor and a dedicated image processing. For that purpose, a versatile imager model is presented and applied to a time-to-first-spike imager associated with two types of neural networks. Image classification is targeted to assess the system performance, namely the classification accuracy and data throughput. Simulation results depict/show the impact of different key-parameters helping in the choice of the final imaging system architecture.
