In this article, we introduce a tool, Popinns, to implement Deep Neural Networks (DNNs) on fixed point architectures. Popinns takes as input the Tensorflow model of a DNN whose coefficients are floating-point numbers and generates a C code in fixed-point arithmetic. The approach implemented in Popinns is based on a formal semantics describing the propagation of the errors through the computations performed by the network. From this semantics, we deduce a system of constraints made of inequalities between linear expressions among integers and of min and max operations. The solution of this system, computed by an optimizing SMT solver, gives the optimal formats of the fixed-point numbers at each point of the DNN. As a result, we synthesize a fixed-point C code that satisfies an error bound set by the user with respect to the initial Tensorflow model. The present article describes Popinns architecture, its features as well as the intermediary and final results computed by the tool.