  New intelligence applications are driving increasing interest in deploying
deep neural networks (DNN) in a distributed way. To set up distributed deep
learning involves alterations of a great number of the parameter configurations
of network/edge devices and DNN models, which are crucial to achieve best
performances. Simulations measure scalability of intelligence applications in
the early stage, as well as to determine the effects of different
configurations, thus highly desired. However, work on simulating the
distributed intelligence environment is still in its infancy. The existing
simulation frameworks, such as NS-3, etc., cannot extended in a straightforward
way to support simulations of distributed learning. In this paper, we propose a
novel discrete event simulator, sim4DistrDL, which includes a deep learning
module and a network simulation module to facilitate simulation of DNN-based
distributed applications. Specifically, we give the design and implementation
of the proposed learning simulator and present an illustrative use case.
