Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fault Tolerance in Distributed Neural Computing   With the increasing complexity of computing systems, complete hardware
reliability can no longer be guaranteed. We need, however, to ensure overall
system reliability. One of the most important features of artificial neural
networks is their intrinsic fault-tolerance. The aim of this work is to
investigate whether such networks have features that can be applied to wider
computational systems. This paper presents an analysis, in both the learning
and operational phases, of a distributed feed-forward neural network with
decentralised event-driven time management, which is insensitive to
intermittent faults caused by unreliable communication or faulty hardware
components. The learning rules used in the model are local in space and time,
which allows efficient scalable distributed implementation. We investigate the
overhead caused by injected faults and analyse the sensitivity to limited
failures in the computational hardware in different areas of the network.

21
The categories of this paper are: adap-org
25
The categories of this paper are: patt-sol
2
The categories of this paper are: math
24
The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
33
The categories of this paper are: plasm-ph
20
The categories of this paper are: acc-phys
17
The categories of this paper are: q-fin
10
The categories of this paper are: q-bio
26
The categories of this paper are: bayes-an
29
The categories of this paper are: funct-an
16
The categories of this paper are: stat