  Neuromorphic computing leverages the sparsity of temporal data to reduce
processing energy by activating a small subset of neurons and synapses at each
time step. When deployed for split computing in edge-based systems, remote
neuromorphic processing units (NPUs) can reduce the communication power budget
by communicating asynchronously using sparse impulse radio (IR) waveforms. This
way, the input signal sparsity translates directly into energy savings both in
terms of computation and communication. However, with IR transmission, the main
contributor to the overall energy consumption remains the power required to
maintain the main radio on. This work proposes a novel architecture that
integrates a wake-up radio mechanism within a split computing system consisting
of remote, wirelessly connected, NPUs. A key challenge in the design of a
wake-up radio-based neuromorphic split computing system is the selection of
thresholds for sensing, wake-up signal detection, and decision making. To
address this problem, as a second contribution, this work proposes a novel
methodology that leverages the use of a digital twin (DT), i.e., a simulator,
of the physical system, coupled with a sequential statistical testing approach
known as Learn Then Test (LTT) to provide theoretical reliability guarantees.
The proposed DT-LTT methodology is broadly applicable to other design problems,
and is showcased here for neuromorphic communications. Experimental results
validate the design and the analysis, confirming the theoretical reliability
guarantees and illustrating trade-offs among reliability, energy consumption,
and informativeness of the decisions.
