A Spiking Neural Algorithm for Network Flow
Keywords
Loading...
Authors
Issue Date
2019-06-05
Language
en
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
It is not clear what the potential is of neuromorphic hardware beyond machine learning and neuroscience.
In this project, a problem is investigated that is inherently difficult to fully implement
in neuromorphic hardware by introducing a new machine model in which a conventional Turing machine
and neuromorphic oracle work together to solve such types of problems. A lattice of complexity
classes is introduced: CSNN(RS), in which a neuromorphic oracle is consulted using only resources at
most RS. We show that the P-complete MAX NETWORK FLOW problem is in LSNN(O(n);O(n);O(n))
for graphs with n edges. A modified variant of this algorithm is implemented on the Intel Loihi chip;
a neuromorphic manycore processor developed by Intel Labs. We show that by off-loading the search
for augmenting paths to the neuromorphic processor we can get energy efficiency gains, while not
sacrificing runtime resources. This result demonstrates how P-complete problems can be mapped on
neuromorphic architectures in a theoretically and potentially practically efficient manner.
Description
Citation
Supervisor
Faculty
Faculteit der Sociale Wetenschappen