Model reduction of feed forward neural networks for resource-constrained devices

Evangelia Fragkou, Marianna K. Sayeg, Dimitrios Katsaros

Abstract: Multilayer neural architectures with a complete bipartite topology have very high training time and memory requirements. Solid evidence suggests that not every connection contributes to the performance; thus, network sparsification has emerged. We get inspiration from the topology of real biological neural networks which are scale-free. We depart from the usual complete bipartite topology among layers, and instead we start from structured sparse topologies known in network science, e.g., scale-free and end up again in a structured sparse topology, e.g., scale-free. Moreover, we apply smart link rewiring methods to construct these sparse topologies. Thus, the number of trainable parameters is reduced, with a direct impact on lowering training time and a direct beneficial result in reducing memory requirements. We design several variants of our concept (SF2SFrand, SF2SFba, SF2SF5, SF2SW, and SW2SW, respectively) by considering the neural network topology as a Scale-Free or Small-World one in every case. We conduct experiments by cutting and stipulating the replacing method of the 30% of the linkages on the network in every epoch. Our winning method, namely the one starting from a scale-free topology and producing a scale-free-like topology (SF2SFrand) can reduce training time without sacrificing neural network accuracy and also cutting memory requirements for the storage of the neural network.