Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: End-to-End Learning of Neuromorphic Wireless Systems for Low-Power Edge
  Artificial Intelligence   This paper introduces a novel "all-spike" low-power solution for remote
wireless inference that is based on neuromorphic sensing, Impulse Radio (IR),
and Spiking Neural Networks (SNNs). In the proposed system, event-driven
neuromorphic sensors produce asynchronous time-encoded data streams that are
encoded by an SNN, whose output spiking signals are pulse modulated via IR and
transmitted over general frequence-selective channels; while the receiver's
inputs are obtained via hard detection of the received signals and fed to an
SNN for classification. We introduce an end-to-end training procedure that
treats the cascade of encoder, channel, and decoder as a probabilistic
SNN-based autoencoder that implements Joint Source-Channel Coding (JSCC). The
proposed system, termed NeuroJSCC, is compared to conventional synchronous
frame-based and uncoded transmissions in terms of latency and accuracy. The
experiments confirm that the proposed end-to-end neuromorphic edge architecture
provides a promising framework for efficient and low-latency remote sensing,
communication, and inference.

35
The categories of this paper are: mtrl-th
4
The categories of this paper are: cond-mat
29
The categories of this paper are: funct-an
9
The categories of this paper are: nlin
11
The categories of this paper are: hep-lat
32
The categories of this paper are: atom-ph
14
The categories of this paper are: math-ph
6
The categories of this paper are: astro-ph
17
The categories of this paper are: q-fin
16
The categories of this paper are: stat
22
The categories of this paper are: chao-dyn
7
The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph
24
The categories of this paper are: dg-ga
10
The categories of this paper are: q-bio
26
The categories of this paper are: bayes-an