  In this paper, we describe a conceptual design methodology to design
distributed neural network architectures that can perform efficient inference
within sensor networks with communication bandwidth constraints. The different
sensor channels are distributed across multiple sensor devices, which have to
exchange data over bandwidth-limited communication channels to solve, e.g., a
classification task. Our design methodology starts from a user-defined
centralized neural network and transforms it into a distributed architecture in
which the channels are distributed over different nodes. The distributed
network consists of two parallel branches of which the outputs are fused at the
fusion center. The first branch collects classification results from local,
node-specific classifiers while the second branch compresses each node's signal
and then reconstructs the multi-channel time series for classification at the
fusion center. We further improve bandwidth gains by dynamically activating the
compression path when the local classifications do not suffice. We validate
this method on a motor execution task in an emulated EEG sensor network and
analyze the resulting bandwidth-accuracy trade-offs. Our experiments show that
the proposed framework enables up to a factor 20 in bandwidth reduction with
minimal loss (up to 2%) in classification accuracy compared to the centralized
baseline on the demonstrated motor execution task. The proposed method offers a
way to smoothly transform a centralized architecture to a distributed,
bandwidth-efficient network amenable for low-power sensor networks. While the
application focus of this paper is on wearable brain-computer interfaces, the
proposed methodology can be applied in other sensor network-like applications
as well.
