Neuromorphic Wireless Device-Edge Co-Inference via the Directed
  Information Bottleneck
Yuzhen Ke Zoran Utkovski Mehdi Heshmati Osvaldo Simeone Johannes Dommel Slawomir Stanczak
An important use case of next-generation wireless systems is device-edge
co-inference, where a semantic task is partitioned between a device and an edge
server. The device carries out data collection and partial processing of the
data, while the remote server completes the given task based on information
received from the device. It is often required that processing and
communication be run as efficiently as possible at the device, while more
computing resources are available at the edge. To address such scenarios, we
introduce a new system solution, termed neuromorphic wireless device-edge
co-inference. According to it, the device runs sensing, processing, and
communication units using neuromorphic hardware, while the server employs
conventional radio and computing technologies. The proposed system is designed
using a transmitter-centric information-theoretic criterion that targets a
reduction of the communication overhead, while retaining the most relevant
information for the end-to-end semantic task of interest. Numerical results on
standard data sets validate the proposed architecture, and a preliminary
testbed realization is reported.