Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Run-time Mapping of Spiking Neural Networks to Neuromorphic Hardware   In this paper, we propose a design methodology to partition and map the
neurons and synapses of online learning SNN-based applications to neuromorphic
architectures at {run-time}. Our design methodology operates in two steps --
step 1 is a layer-wise greedy approach to partition SNNs into clusters of
neurons and synapses incorporating the constraints of the neuromorphic
architecture, and step 2 is a hill-climbing optimization algorithm that
minimizes the total spikes communicated between clusters, improving energy
consumption on the shared interconnect of the architecture. We conduct
experiments to evaluate the feasibility of our algorithm using synthetic and
realistic SNN-based applications. We demonstrate that our algorithm reduces SNN
mapping time by an average 780x compared to a state-of-the-art design-time
based SNN partitioning approach with only 6.25\% lower solution quality.

8
The categories of this paper are: hep-ex
4
The categories of this paper are: cond-mat
23
The categories of this paper are: solv-int
15
The categories of this paper are: nucl-ex
5
The categories of this paper are: gr-qc
21
The categories of this paper are: adap-org
37
The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci
16
The categories of this paper are: stat
24
The categories of this paper are: dg-ga
7
The categories of this paper are: hep-th
3
The categories of this paper are: physics
36
The categories of this paper are: cmp-lg
12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
0
The categories of this paper are: hep-ph